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Brown RE. Measuring the replicability of our own research. J Neurosci Methods 2024; 406:110111. [PMID: 38521128 DOI: 10.1016/j.jneumeth.2024.110111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Revised: 03/08/2024] [Accepted: 03/18/2024] [Indexed: 03/25/2024]
Abstract
In the study of transgenic mouse models of neurodevelopmental and neurodegenerative disorders, we use batteries of tests to measure deficits in behaviour and from the results of these tests, we make inferences about the mental states of the mice that we interpret as deficits in "learning", "memory", "anxiety", "depression", etc. This paper discusses the problems of determining whether a particular transgenic mouse is a valid mouse model of disease X, the problem of background strains, and the question of whether our behavioural tests are measuring what we say they are. The problem of the reliability of results is then discussed: are they replicable between labs and can we replicate our results in our own lab? This involves the study of intra- and inter- experimenter reliability. The variables that influence replicability and the importance of conducting a complete behavioural phenotype: sensory, motor, cognitive and social emotional behaviour are discussed. Then the thorny question of failure to replicate is examined: Is it a curse or a blessing? Finally, the role of failure in research and what it tells us about our research paradigms is examined.
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Affiliation(s)
- Richard E Brown
- Department of Psychology and Neuroscience, Dalhousie University, Halifax, NS B3H 4R2, Canada.
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2
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Bellomo RK, Zavalis EA, Ioannidis JPA. Assessment of transparency indicators in space medicine. PLoS One 2024; 19:e0300701. [PMID: 38564591 PMCID: PMC10986997 DOI: 10.1371/journal.pone.0300701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
Space medicine is a vital discipline with often time-intensive and costly projects and constrained opportunities for studying various elements such as space missions, astronauts, and simulated environments. Moreover, private interests gain increasing influence in this discipline. In scientific disciplines with these features, transparent and rigorous methods are essential. Here, we undertook an evaluation of transparency indicators in publications within the field of space medicine. A meta-epidemiological assessment of PubMed Central Open Access (PMC OA) eligible articles within the field of space medicine was performed for prevalence of code sharing, data sharing, pre-registration, conflicts of interest, and funding. Text mining was performed with the rtransparent text mining algorithms with manual validation of 200 random articles to obtain corrected estimates. Across 1215 included articles, 39 (3%) shared code, 258 (21%) shared data, 10 (1%) were registered, 110 (90%) contained a conflict-of-interest statement, and 1141 (93%) included a funding statement. After manual validation, the corrected estimates for code sharing, data sharing, and registration were 5%, 27%, and 1%, respectively. Data sharing was 32% when limited to original articles and highest in space/parabolic flights (46%). Overall, across space medicine we observed modest rates of data sharing, rare sharing of code and almost non-existent protocol registration. Enhancing transparency in space medicine research is imperative for safeguarding its scientific rigor and reproducibility.
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Affiliation(s)
- Rosa Katia Bellomo
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, United States of America
- Department of Public Health and Infectious Diseases, Sapienza University of Rome, Rome, Italy
| | - Emmanuel A. Zavalis
- Department of Learning Informatics Management and Ethics, Karolinska Institutet, Stockholm, Sweden
- Departments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, Stanford University, Stanford, CA, United States of America
| | - John P. A. Ioannidis
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, United States of America
- Departments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, Stanford University, Stanford, CA, United States of America
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3
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Jiang L, Lan M, Menke JD, Vorland CJ, Kilicoglu H. CONSORT-TM: Text classification models for assessing the completeness of randomized controlled trial publications. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.31.24305138. [PMID: 38633775 PMCID: PMC11023672 DOI: 10.1101/2024.03.31.24305138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Objective To develop text classification models for determining whether the checklist items in the CONSORT reporting guidelines are reported in randomized controlled trial publications. Materials and Methods Using a corpus annotated at the sentence level with 37 fine-grained CONSORT items, we trained several sentence classification models (PubMedBERT fine-tuning, BioGPT fine-tuning, and in-context learning with GPT-4) and compared their performance. To address the problem of small training dataset, we used several data augmentation methods (EDA, UMLS-EDA, text generation and rephrasing with GPT-4) and assessed their impact on the fine-tuned PubMedBERT model. We also fine-tuned PubMedBERT models limited to checklist items associated with specific sections (e.g., Methods) to evaluate whether such models could improve performance compared to the single full model. We performed 5-fold cross-validation and report precision, recall, F1 score, and area under curve (AUC). Results Fine-tuned PubMedBERT model that takes as input the sentence and the surrounding sentence representations and uses section headers yielded the best overall performance (0.71 micro-F1, 0.64 macro-F1). Data augmentation had limited positive effect, UMLS-EDA yielding slightly better results than data augmentation using GPT-4. BioGPT fine-tuning and GPT-4 in-context learning exhibited suboptimal results. Methods-specific model yielded higher performance for methodology items, other section-specific models did not have significant impact. Conclusion Most CONSORT checklist items can be recognized reasonably well with the fine-tuned PubMedBERT model but there is room for improvement. Improved models can underpin the journal editorial workflows and CONSORT adherence checks and can help authors in improving the reporting quality and completeness of their manuscripts.
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Affiliation(s)
- Lan Jiang
- School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, IL, USA
| | - Mengfei Lan
- School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, IL, USA
| | - Joe D. Menke
- School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, IL, USA
| | - Colby J Vorland
- Indiana University, School of Public Health, Bloomington, IN, USA
| | - Halil Kilicoglu
- School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, IL, USA
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Hankenson FC, Prager EM, Berridge BR. Advocating for Generalizability: Accepting Inherent Variability in Translation of Animal Research Outcomes. Annu Rev Anim Biosci 2024; 12:391-410. [PMID: 38358839 DOI: 10.1146/annurev-animal-021022-043531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
Advancing scientific discovery requires investigators to embrace research practices that increase transparency and disclosure about materials, methods, and outcomes. Several research advocacy and funding organizations have produced guidelines and recommended practices to enhance reproducibility through detailed and rigorous research approaches; however, confusion around vocabulary terms and a lack of adoption of suggested practices have stymied successful implementation. Although reproducibility of research findings cannot be guaranteed due to extensive inherent variables in attempts at experimental repetition, the scientific community can advocate for generalizability in the application of data outcomes to ensure a broad and effective impact on the comparison of animals to translation within human research. This report reviews suggestions, based upon work with National Institutes of Health advisory groups, for improving rigor and transparency in animal research through aspects of experimental design, statistical assessment, and reporting factors to advocate for generalizability in the application of comparative outcomes between animals and humans.
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Affiliation(s)
- F C Hankenson
- Division of Laboratory Animal Medicine, Department of Pathobiology, School of Veterinary Medicine and University Laboratory Animal Resources, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
| | - E M Prager
- Research Program Management, Regeneron Pharmaceuticals, Inc., Tarrytown, New York, USA;
| | - B R Berridge
- B2 Pathology Solutions LLC, Cary, North Carolina, USA;
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5
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Bullock GS, Ward P, Impellizzeri FM, Kluzek S, Hughes T, Hillman C, Waterman BR, Danelson K, Henry K, Barr E, Healy K, Räisänen AM, Gomez C, Fernandez G, Wolf J, Nicholson KF, Sell T, Zerega R, Dhiman P, Riley RD, Collins GS. Up Front and Open? Shrouded in Secrecy? Or Somewhere in Between? A Meta-Research Systematic Review of Open Science Practices in Sport Medicine Research. J Orthop Sports Phys Ther 2023; 53:1-13. [PMID: 37860866 DOI: 10.2519/jospt.2023.12016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
OBJECTIVE: To investigate open science practices in research published in the top 5 sports medicine journals from May 1, 2022, and October 1, 2022. DESIGN: A meta-research systematic review. LITERATURE SEARCH: Open science practices were searched in MEDLINE. STUDY SELECTION CRITERIA: We included original scientific research published in one of the identified top 5 sports medicine journals in 2022 as ranked by Clarivate: (1) British Journal of Sports Medicine, (2) Journal of Sport and Health Science, (3) American Journal of Sports Medicine, (4) Medicine and Science in Sports and Exercise, and (5) Sports Medicine-Open. Studies were excluded if they were systematic reviews, qualitative research, gray literature, or animal or cadaver models. DATA SYNTHESIS: Open science practices were extracted in accordance with the Transparency and Openness Promotion guidelines and patient and public involvement. RESULTS: Two hundred forty-three studies were included. The median number of open science practices in each study was 2, out of a maximum of 12 (range: 0-8; interquartile range: 2). Two hundred thirty-four studies (96%, 95% confidence interval [CI]: 94%-99%) provided an author conflict-of-interest statement and 163 (67%, 95% CI: 62%-73%) reported funding. Twenty-one studies (9%, 95% CI: 5%-12%) provided open-access data. Fifty-four studies (22%, 95% CI: 17%-27%) included a data availability statement and 3 (1%, 95% CI: 0%-3%) made code available. Seventy-six studies (32%, 95% CI: 25%-37%) had transparent materials and 30 (12%, 95% CI: 8%-16%) used a reporting guideline. Twenty-eight studies (12%, 95% CI: 8%-16%) were preregistered. Six studies (3%, 95% CI: 1%-4%) published a protocol. Four studies (2%, 95% CI: 0%-3%) reported an analysis plan a priori. Seven studies (3%, 95% CI: 1%-5%) reported patient and public involvement. CONCLUSION: Open science practices in the sports medicine field are extremely limited. The least followed practices were sharing code, data, and analysis plans. J Orthop Sports Phys Ther 2023;53(12):1-13. Epub 20 October 2023. doi:10.2519/jospt.2023.12016.
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Affiliation(s)
- Garrett S Bullock
- Department of Orthopaedic Surgery & Rehabilitation, Wake Forest School of Medicine, Winston-Salem, NC
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC
- Centre for Sport, Exercise and Osteoarthritis Research Versus Arthritis, University of Oxford, Oxford, United Kingdom
- Sport Injury Prevention Research Center, University of Calgary, Calgary, AB, Canada
| | | | - Franco M Impellizzeri
- School of Sport, Exercise, and Rehabilitation, University of Technology Sydney, Sydney, Australia
| | - Stefan Kluzek
- Centre for Sport, Exercise and Osteoarthritis Research Versus Arthritis, University of Oxford, Oxford, United Kingdom
- Sports Medicine Research Department, University of Nottingham, Nottingham, UK
- English Institute of Sport, Marlow, United Kingdom
| | - Tom Hughes
- Department of Health Professions, Manchester Metropolitan University, Manchester, United Kingdom
| | - Charles Hillman
- Sports Medicine Research Department, University of Nottingham, Nottingham, UK
| | - Brian R Waterman
- Department of Orthopaedic Surgery & Rehabilitation, Wake Forest School of Medicine, Winston-Salem, NC
| | - Kerry Danelson
- Department of Orthopaedic Surgery & Rehabilitation, Wake Forest School of Medicine, Winston-Salem, NC
| | - Kaitlin Henry
- Department of Orthopaedic Surgery & Rehabilitation, Wake Forest School of Medicine, Winston-Salem, NC
| | - Emily Barr
- Department of Orthopaedic Surgery & Rehabilitation, Wake Forest School of Medicine, Winston-Salem, NC
| | - Kelsey Healy
- Department of Orthopaedic Surgery & Rehabilitation, Wake Forest School of Medicine, Winston-Salem, NC
| | - Anu M Räisänen
- Department of Physical Therapy Education - Oregon, College of Health Sciences-Northwest, Western University of Health Sciences, Lebanon, OR
- Sport Injury Prevention Research Centre, Faculty of Kinesiology, University of Calgary, Calgary, AB, Canada
| | - Christina Gomez
- Department of Physical Therapy Education - Oregon, College of Health Sciences-Northwest, Western University of Health Sciences, Lebanon, OR
| | - Garrett Fernandez
- Department of Orthopaedic Surgery & Rehabilitation, Wake Forest School of Medicine, Winston-Salem, NC
| | - Jakob Wolf
- Department of Orthopaedic Surgery & Rehabilitation, Wake Forest School of Medicine, Winston-Salem, NC
| | - Kristen F Nicholson
- Department of Orthopaedic Surgery & Rehabilitation, Wake Forest School of Medicine, Winston-Salem, NC
| | | | | | - Paula Dhiman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom
| | - Richard D Riley
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom
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Lubega N, Anderson A, Nelson NC. Experience of irreproducibility as a risk factor for poor mental health in biomedical science doctoral students: A survey and interview-based study. PLoS One 2023; 18:e0293584. [PMID: 37967083 PMCID: PMC10651026 DOI: 10.1371/journal.pone.0293584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 10/08/2023] [Indexed: 11/17/2023] Open
Abstract
High rates of irreproducibility and of poor mental health in graduate students have been reported in the biomedical sciences in the past ten years, but to date, little research has investigated whether these two trends interact. In this study, we ask whether the experience of failing to replicate an expected finding impacts graduate students' mental health. Using an online survey paired with semi-structured qualitative interviews, we examined how often biomedical science doctoral students at a large American public university experienced events that could be interpreted as failures to replicate and how they responded to these experiences. We found that almost all participants had experience with irreproducibility: 84% had failed to replicate their own results, 70% had failed to replicate a colleague's finding, and 58% had failed to replicate a result from the published literature. Participants reported feelings of self-doubt, frustration, and depression while experiencing irreproducibility, and in 24% of cases, these emotional responses were strong enough to interfere with participants' eating, sleeping, or ability to work. A majority (82%) of participants initially believed that the anomalous results could be attributed to their own error. However, after further experimentation, most participants concluded that the original result was wrong (38%), that there was a key difference between the original experiment and their own (17%), or that there was a problem with the protocol (17%). These results suggest that biomedical science graduate students may be biased towards initially interpreting failures to replicate as indicative of a lack of skill, which may trigger or perpetuate feelings of anxiety, depression, or impostorism.
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Affiliation(s)
- Nasser Lubega
- University of Wisconsin–Madison School of Medicine and Public Health, Madison WI, United States of America
| | - Abigail Anderson
- Midwestern University Chicago College of Osteopathic Medicine, Chicago IL, United States of America
| | - Nicole C. Nelson
- University of Wisconsin–Madison School of Medicine and Public Health, Madison WI, United States of America
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Carvalho RM, de Magalhães-Barbosa MC, Bianchi LM, Rodrigues-Santos G, da Cunha AJLA, Bastos FI, Prata-Barbosa A. Shift in hospital opioid use during the COVID-19 pandemic in Brazil: a time-series analysis of one million prescriptions. Sci Rep 2023; 13:17197. [PMID: 37821638 PMCID: PMC10567754 DOI: 10.1038/s41598-023-44533-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 10/10/2023] [Indexed: 10/13/2023] Open
Abstract
The pronounced change in the profile of hospitalized patients during COVID-19 and the severe respiratory component of this disease, with a great need for mechanical ventilation, led to changes in the consumption pattern of some medicines and supplies. This time-series study analyzed the in-hospital consumption of opioids during the COVID-19 pandemic in 24 Brazilian hospitals compared to the pre-pandemic period. Data included 711,883 adult patients who had opioids prescribed. In 2020, the mean consumption was significantly higher compared to 2019 for parenteral fentanyl, enteral methadone, and parenteral methadone. It was significantly lower for parenteral morphine parenteral sufentanil, and parenteral tramadol. For remifentanil, it did not differ. The number of patients in 2020 was lower but the mean consumption was higher for fentanyl, parenteral methadone, and remifentanil. It was lower for enteral methadone and parenteral sufentanil. The consumption of parenteral morphine and parenteral tramadol was stable. There was a relevant increase in hospital consumption of some potent opioids during the COVID-19 pandemic in Brazil. These results reinforce the concern about epidemiological surveillance of opioid use after periods of increased hospital use since in-hospital consumption can be the gateway to the misuse or other than the prescribed use of opioids after discharge.
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Affiliation(s)
- Romulo Mendonça Carvalho
- Doctoral Program in Medical Sciences, D'Or Institute for Research & Education (IDOR), Rio de Janeiro, RJ, 22281-100, Brazil
- Pharmaceutical Division, Rede D'Or São Luiz, Rio de Janeiro, RJ, 22270-010, Brazil
| | | | - Lucas Monteiro Bianchi
- Doctoral Program in Epidemiology in Public Health, National School of Public Health Sergio Arouca (ENSP), Rio de Janeiro, RJ, 21041-210, Brazil
| | - Gustavo Rodrigues-Santos
- Department of Pediatrics, D'Or Institute for Research & Education (IDOR), Rio de Janeiro, RJ, 22281-100, Brazil
- Doctoral Program in Collective Health, Institute of Social Medicine (IMS), State University of Rio de Janeiro (UERJ), Rio de Janeiro, RJ, Brazil
| | - Antônio José Ledo Alves da Cunha
- Department of Pediatrics, D'Or Institute for Research & Education (IDOR), Rio de Janeiro, RJ, 22281-100, Brazil
- Department of Pediatrics, School of Medicine, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, 21044-020, Brazil
| | - Francisco Inácio Bastos
- Laboratory of Health Information, Institute of Scientific and Technological Communication and Information in Health, Oswaldo Cruz Foundation (IOC), Rio de Janeiro, RJ, 21040-900, Brazil
| | - Arnaldo Prata-Barbosa
- Department of Pediatrics, D'Or Institute for Research & Education (IDOR), Rio de Janeiro, RJ, 22281-100, Brazil.
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van 't Hooft J, van Dijk CE, Axfors C, Alfirevic Z, Oudijk MA, Mol BWJ, Bossuyt PM, Ioannidis JPA. Few randomized trials in preterm birth prevention meet predefined usefulness criteria. J Clin Epidemiol 2023; 162:107-117. [PMID: 37657614 DOI: 10.1016/j.jclinepi.2023.08.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 07/03/2023] [Accepted: 08/24/2023] [Indexed: 09/03/2023]
Abstract
OBJECTIVES We operationalized a research usefulness tool identified through literature searches and consensus and examined if randomized controlled trials (RCTs) addressing preterm birth prevention met predefined criteria for usefulness. STUDY DESIGN AND SETTING The usefulness tool included eight criteria combining 13 items. RCTs were evaluated for compliance with each item by multiple assessors (reviewer agreement 95-98%). Proportions of compliances with 95% confidence interval (CI) were calculated and change over time was assessed using ≧ 2010 as a cutoff. RESULTS Among 347 selected RCTs, published within 56 preterm birth Cochrane reviews, only 36 (10%, 95% CI = 7-14%) met more than half of the usefulness criteria. Compared to trials before 2010, recent trials used composite or surrogate (less informative) outcomes more often (13% vs. 25%, relative risk 1.91, 95% CI = 1.21-3.00). Only 16 trials reflected real practice (pragmatism) in design (5%, 95% CI = 3-7%), with no improvements over time. No trials reported involvement of mothers to reflect patients' research priorities and outcomes selection. Recent trials were more transparent. CONCLUSION Few preterm birth prevention RCTs met more than half of the usefulness criteria but most of usefulness criteria are improving after 2010. Use of informative outcomes, patient centeredness, pragmatism and transparency should be key targets for future research planning.
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Affiliation(s)
- Janneke van 't Hooft
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Palo Alto, CA, USA; Department of Obstetrics and Gynecology, Amsterdam UMC, University of Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.
| | - Charlotte E van Dijk
- Department of Obstetrics and Gynecology, Amsterdam UMC, University of Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Cathrine Axfors
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Palo Alto, CA, USA; Department for Women's and Children's Health, Uppsala University, Uppsala, Sweden
| | - Zarko Alfirevic
- Center for Women's Health Research, Liverpool Women's Hospital, Liverpool, United Kingdom
| | - Martijn A Oudijk
- Department of Obstetrics and Gynecology, Amsterdam UMC, University of Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, Netherlands; Amsterdam Reproduction and Development Institute, Amsterdam, The Netherlands
| | - Ben W J Mol
- Department of Obstetrics and Gynecology, Monash University, Melbourne, Victoria, Australia; Aberdeen Centre for Women's Health Research, School of Medicine, University of Aberdeen, Aberdeen, UK
| | - Patrick M Bossuyt
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam AUMC, University of Amsterdam, Amsterdam, The Netherlands
| | - John P A Ioannidis
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Palo Alto, CA, USA; Departments of Medicine, of Epidemiology and Population Health of Biomedical Data Science, and of Statistics, Stanford University, Palo Alto, CA, USA
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Kocak B, Yardimci AH, Yuzkan S, Keles A, Altun O, Bulut E, Bayrak ON, Okumus AA. Transparency in Artificial Intelligence Research: a Systematic Review of Availability Items Related to Open Science in Radiology and Nuclear Medicine. Acad Radiol 2023; 30:2254-2266. [PMID: 36526532 DOI: 10.1016/j.acra.2022.11.030] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 11/21/2022] [Accepted: 11/22/2022] [Indexed: 12/15/2022]
Abstract
RATIONALE AND OBJECTIVES Reproducibility of artificial intelligence (AI) research has become a growing concern. One of the fundamental reasons is the lack of transparency in data, code, and model. In this work, we aimed to systematically review the radiology and nuclear medicine papers on AI in terms of transparency and open science. MATERIALS AND METHODS A systematic literature search was performed in PubMed to identify original research studies on AI. The search was restricted to studies published in Q1 and Q2 journals that are also indexed on the Web of Science. A random sampling of the literature was performed. Besides six baseline study characteristics, a total of five availability items were evaluated. Two groups of independent readers including eight readers participated in the study. Inter-rater agreement was analyzed. Disagreements were resolved with consensus. RESULTS Following eligibility criteria, we included a final set of 194 papers. The raw data was available in about one-fifth of the papers (34/194; 18%). However, the authors made their private data available only in one paper (1/161; 1%). About one-tenth of the papers made their pre-modeling (25/194; 13%), modeling (28/194; 14%), or post-modeling files (15/194; 8%) available. Most of the papers (189/194; 97%) did not attempt to create a ready-to-use system for real-world usage. Data origin, use of deep learning, and external validation had statistically significantly different distributions. The use of private data alone was negatively associated with the availability of at least one item (p<0.001). CONCLUSION Overall rates of availability for items were poor, leaving room for substantial improvement.
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Affiliation(s)
- Burak Kocak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, 34480, Istanbul, Turkey.
| | - Aytul Hande Yardimci
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, 34480, Istanbul, Turkey
| | - Sabahattin Yuzkan
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, 34480, Istanbul, Turkey
| | - Ali Keles
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, 34480, Istanbul, Turkey
| | - Omer Altun
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, 34480, Istanbul, Turkey
| | - Elif Bulut
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, 34480, Istanbul, Turkey
| | - Osman Nuri Bayrak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, 34480, Istanbul, Turkey
| | - Ahmet Arda Okumus
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, 34480, Istanbul, Turkey
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10
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Kilicoglu H, Jiang L, Hoang L, Mayo-Wilson E, Vinkers CH, Otte WM. Methodology reporting improved over time in 176,469 randomized controlled trials. J Clin Epidemiol 2023; 162:19-28. [PMID: 37562729 PMCID: PMC10829891 DOI: 10.1016/j.jclinepi.2023.08.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 07/25/2023] [Accepted: 08/02/2023] [Indexed: 08/12/2023]
Abstract
OBJECTIVES To describe randomized controlled trial (RCT) methodology reporting over time. STUDY DESIGN AND SETTING We used a deep learning-based sentence classification model based on the Consolidated Standards of Reporting Trials (CONSORT) statement, considered minimum requirements for reporting RCTs. We included 176,469 RCT reports published between 1966 and 2018. We analyzed the reporting trends over 5-year time periods, grouping trials from 1966 to 1990 in a single stratum. We also explored the effect of journal impact factor (JIF) and medical discipline. RESULTS Population, Intervention, Comparator, Outcome (PICO) items were commonly reported during each period, and reporting increased over time (e.g., interventions: 79.1% during 1966-1990 to 87.5% during 2010-2018). Reporting of some methods information has increased, although there is room for improvement (e.g., sequence generation: 10.8-41.8%). Some items are reported infrequently (e.g., allocation concealment: 5.1-19.3%). The number of items reported and JIF are weakly correlated (Pearson's r (162,702) = 0.16, P < 0.001). The differences in the proportion of items reported between disciplines are small (<10%). CONCLUSION Our analysis provides large-scale quantitative support for the hypothesis that RCT methodology reporting has improved over time. Extending these models to all CONSORT items could facilitate compliance checking during manuscript authoring and peer review, and support metaresearch.
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Affiliation(s)
- Halil Kilicoglu
- School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, IL, USA.
| | - Lan Jiang
- School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, IL, USA
| | - Linh Hoang
- School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, IL, USA
| | - Evan Mayo-Wilson
- Department of Epidemiology, University of North Carolina School of Global Public Health, Chapel Hill, NC, USA
| | - Christiaan H Vinkers
- Department of Psychiatry and Anatomy & Neurosciences, Amsterdam University Medical Center Location Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands; Amsterdam Public Health, Mental Health Program and Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress Program, Amsterdam, The Netherlands; GGZ inGeest Mental Health Care, 1081 HJ, Amsterdam, The Netherlands
| | - Willem M Otte
- Department of Child Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, and Utrecht University, Utrecht, The Netherlands
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El Kababji S, Mitsakakis N, Fang X, Beltran-Bless AA, Pond G, Vandermeer L, Radhakrishnan D, Mosquera L, Paterson A, Shepherd L, Chen B, Barlow WE, Gralow J, Savard MF, Clemons M, El Emam K. Evaluating the Utility and Privacy of Synthetic Breast Cancer Clinical Trial Data Sets. JCO Clin Cancer Inform 2023; 7:e2300116. [PMID: 38011617 PMCID: PMC10703127 DOI: 10.1200/cci.23.00116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 08/24/2023] [Accepted: 09/19/2023] [Indexed: 11/29/2023] Open
Abstract
PURPOSE There is strong interest from patients, researchers, the pharmaceutical industry, medical journal editors, funders of research, and regulators in sharing clinical trial data for secondary analysis. However, data access remains a challenge because of concerns about patient privacy. It has been argued that synthetic data generation (SDG) is an effective way to address these privacy concerns. There is a dearth of evidence supporting this on oncology clinical trial data sets, and on the utility of privacy-preserving synthetic data. The objective of the proposed study is to validate the utility and privacy risks of synthetic clinical trial data sets across multiple SDG techniques. METHODS We synthesized data sets from eight breast cancer clinical trial data sets using three types of generative models: sequential synthesis, conditional generative adversarial network, and variational autoencoder. Synthetic data utility was evaluated by replicating the published analyses on the synthetic data and assessing concordance of effect estimates and CIs between real and synthetic data. Privacy was evaluated by measuring attribution disclosure risk and membership disclosure risk. RESULTS Utility was highest using the sequential synthesis method where all results were replicable and the CI overlap most similar or higher for seven of eight data sets. Both types of privacy risks were low across all three types of generative models. DISCUSSION Synthetic data using sequential synthesis methods can act as a proxy for real clinical trial data sets, and simultaneously have low privacy risks. This type of generative model can be one way to enable broader sharing of clinical trial data.
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Affiliation(s)
| | | | - Xi Fang
- Replica Analytics Ltd, Ottawa, ON, Canada
| | - Ana-Alicia Beltran-Bless
- Ottawa Hospital Research Institute, Ottawa, ON, Canada
- Division of Medical Oncology, Department of Medicine, University of Ottawa, ON, Canada
| | - Greg Pond
- McMaster University, Hamilton, ON, Canada
| | | | - Dhenuka Radhakrishnan
- CHEO Research Institute, Ottawa, ON, Canada
- Department of Paediatrics, University of Ottawa, Ottawa, ON, Canada
| | - Lucy Mosquera
- CHEO Research Institute, Ottawa, ON, Canada
- Replica Analytics Ltd, Ottawa, ON, Canada
| | | | | | | | | | | | - Marie-France Savard
- Ottawa Hospital Research Institute, Ottawa, ON, Canada
- Division of Medical Oncology, Department of Medicine, University of Ottawa, ON, Canada
| | - Mark Clemons
- Ottawa Hospital Research Institute, Ottawa, ON, Canada
- Division of Medical Oncology, Department of Medicine, University of Ottawa, ON, Canada
| | - Khaled El Emam
- CHEO Research Institute, Ottawa, ON, Canada
- Replica Analytics Ltd, Ottawa, ON, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
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12
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Zavalis EA, Contopoulos-Ioannidis DG, Ioannidis JPA. Transparency in Infectious Disease Research: Meta-research Survey of Specialty Journals. J Infect Dis 2023; 228:227-234. [PMID: 37132475 DOI: 10.1093/infdis/jiad130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 02/24/2023] [Accepted: 05/01/2023] [Indexed: 05/04/2023] Open
Abstract
BACKGROUND Infectious diseases carry large global burdens and have implications for society at large. Therefore, reproducible, transparent research is extremely important. METHODS We evaluated transparency indicators (code and data sharing, registration, and conflict and funding disclosures) in the 5340 PubMed Central Open Access articles published in 2019 or 2021 in the 9 most cited specialty journals in infectious diseases using the text-mining R package, rtransparent. RESULTS A total of 5340 articles were evaluated (1860 published in 2019 and 3480 in 2021 [of which 1828 were on coronavirus disease 2019, or COVID-19]). Text mining identified code sharing in 98 (2%) articles, data sharing in 498 (9%), registration in 446 (8%), conflict of interest disclosures in 4209 (79%), and funding disclosures in 4866 (91%). There were substantial differences across the 9 journals: 1%-9% for code sharing, 5%-25% for data sharing, 1%-31% for registration, 7%-100% for conflicts of interest, and 65%-100% for funding disclosures. Validation-corrected imputed estimates were 3%, 11%, 8%, 79%, and 92%, respectively. There were no major differences between articles published in 2019 and non-COVID-19 articles in 2021. In 2021, non-COVID-19 articles had more data sharing (12%) than COVID-19 articles (4%). CONCLUSIONS Data sharing, code sharing, and registration are very uncommon in infectious disease specialty journals. Increased transparency is required.
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Affiliation(s)
- Emmanuel A Zavalis
- Department of Learning Informatics Management and Ethics, Karolinska Institutet, Stockholm, Sweden
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University
| | | | - John P A Ioannidis
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University
- Stanford Prevention Research Center, Department of Medicine
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, USA
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13
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Zhao L, Shen C, Liu M, Zhang J, Cheng L, Li Y, Yuan L, Zhang J, Tian J. Comparison of Reporting and Transparency in Published Protocols and Publications in Umbrella Reviews: Scoping Review. J Med Internet Res 2023; 25:e43299. [PMID: 37531172 PMCID: PMC10433027 DOI: 10.2196/43299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 02/15/2023] [Accepted: 05/05/2023] [Indexed: 08/03/2023] Open
Abstract
BACKGROUND Inconsistencies between a protocol and its umbrella review (UR) may mislead readers about the importance of findings or lead to false-positive results. Furthermore, not documenting and explaining inconsistencies in the UR could reduce its transparency. To our knowledge, no study has examined the methodological consistency of the protocols with their URs and assessed the transparency of the URs when generating evidence. OBJECTIVE This study aimed to investigate the inconsistency of protocols with their URs in the methodology and assess the transparency of the URs. METHODS We searched medical-related electronic databases from their inception to January 1, 2022. We investigated inconsistencies between protocols and their publications and transparencies in the search strategy, inclusion criteria, methods of screening and data extraction, quality assessment, and statistical analysis. RESULTS We included 31 protocols and 35 publications. For the search strategy, 39 inconsistencies between the protocols and their publications were found in 26 of the 35 (74%) URs, and 16 of these inconsistencies were indicated and explained. There were 84 inconsistencies between the protocols and their URs regarding the inclusion criteria in 31 of the 35 (89%) URs, and 29 of the inconsistencies were indicated and explained. Deviations from their protocols were found in 12 of the 32 (38%) URs reporting the methods of screening, 14 of the 30 (47%) URs reporting the methods of data extraction, and 11 of the 32 (34%) URs reporting the methods for quality assessment. Of the 35 URs, 6 (17%) were inconsistent with their protocols in terms of the tools for quality assessment; one-half (3/6, 50%) of them indicated and explained the deviations. As for the statistical analysis, 31 of the 35 (89%) URs generated 61 inconsistencies between the publications and their protocols, and 16 inconsistencies were indicated and explained. CONCLUSIONS There was a high prevalence of inconsistencies between protocols and publications of URs, and more than one-half of the inconsistencies were not indicated and explained in the publications. Therefore, how to promote the transparency of URs will be a major part of future work.
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Affiliation(s)
- Liang Zhao
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Caiyi Shen
- The First Clinical Medical College, Lanzhou University, Lanzhou, China
| | - Ming Liu
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
| | - Jiaoyan Zhang
- Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou, China
| | - Luying Cheng
- Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou, China
- Zigong First People's Hospital, Zigong, China
| | - Yuanyuan Li
- Evidence-Based Nursing Center, School of Nursing, Lanzhou University, Lanzhou, China
| | - Lanbin Yuan
- The First Clinical Medical College, Lanzhou University, Lanzhou, China
| | - Junhua Zhang
- Evidence-Based Medicine Center, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Jinhui Tian
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China
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14
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Hamilton DG, Hong K, Fraser H, Rowhani-Farid A, Fidler F, Page MJ. Prevalence and predictors of data and code sharing in the medical and health sciences: systematic review with meta-analysis of individual participant data. BMJ 2023; 382:e075767. [PMID: 37433624 DOI: 10.1136/bmj-2023-075767] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/13/2023]
Abstract
OBJECTIVES To synthesise research investigating data and code sharing in medicine and health to establish an accurate representation of the prevalence of sharing, how this frequency has changed over time, and what factors influence availability. DESIGN Systematic review with meta-analysis of individual participant data. DATA SOURCES Ovid Medline, Ovid Embase, and the preprint servers medRxiv, bioRxiv, and MetaArXiv were searched from inception to 1 July 2021. Forward citation searches were also performed on 30 August 2022. REVIEW METHODS Meta-research studies that investigated data or code sharing across a sample of scientific articles presenting original medical and health research were identified. Two authors screened records, assessed the risk of bias, and extracted summary data from study reports when individual participant data could not be retrieved. Key outcomes of interest were the prevalence of statements that declared that data or code were publicly or privately available (declared availability) and the success rates of retrieving these products (actual availability). The associations between data and code availability and several factors (eg, journal policy, type of data, trial design, and human participants) were also examined. A two stage approach to meta-analysis of individual participant data was performed, with proportions and risk ratios pooled with the Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis. RESULTS The review included 105 meta-research studies examining 2 121 580 articles across 31 specialties. Eligible studies examined a median of 195 primary articles (interquartile range 113-475), with a median publication year of 2015 (interquartile range 2012-2018). Only eight studies (8%) were classified as having a low risk of bias. Meta-analyses showed a prevalence of declared and actual public data availability of 8% (95% confidence interval 5% to 11%) and 2% (1% to 3%), respectively, between 2016 and 2021. For public code sharing, both the prevalence of declared and actual availability were estimated to be <0.5% since 2016. Meta-regressions indicated that only declared public data sharing prevalence estimates have increased over time. Compliance with mandatory data sharing policies ranged from 0% to 100% across journals and varied by type of data. In contrast, success in privately obtaining data and code from authors historically ranged between 0% and 37% and 0% and 23%, respectively. CONCLUSIONS The review found that public code sharing was persistently low across medical research. Declarations of data sharing were also low, increasing over time, but did not always correspond to actual sharing of data. The effectiveness of mandatory data sharing policies varied substantially by journal and type of data, a finding that might be informative for policy makers when designing policies and allocating resources to audit compliance. SYSTEMATIC REVIEW REGISTRATION Open Science Framework doi:10.17605/OSF.IO/7SX8U.
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Affiliation(s)
- Daniel G Hamilton
- MetaMelb Research Group, School of BioSciences, University of Melbourne, Melbourne, VIC, Australia
- Melbourne Medical School, Faculty of Medicine, Dentistry, and Health Sciences, University of Melbourne, Melbourne, VIC, Australia
| | - Kyungwan Hong
- Department of Practice, Sciences, and Health Outcomes Research, University of Maryland School of Pharmacy, Baltimore, MD, USA
| | - Hannah Fraser
- MetaMelb Research Group, School of BioSciences, University of Melbourne, Melbourne, VIC, Australia
| | - Anisa Rowhani-Farid
- Department of Practice, Sciences, and Health Outcomes Research, University of Maryland School of Pharmacy, Baltimore, MD, USA
| | - Fiona Fidler
- MetaMelb Research Group, School of BioSciences, University of Melbourne, Melbourne, VIC, Australia
- School of Historical and Philosophical Studies, University of Melbourne, Melbourne, VIC, Australia
| | - Matthew J Page
- Methods in Evidence Synthesis Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
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15
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Sparks AH, Ponte EMD, Alves KS, Foster ZSL, Grünwald NJ. Openness and Computational Reproducibility in Plant Pathology: Where We Stand and a Way Forward. PHYTOPATHOLOGY 2023; 113:1159-1170. [PMID: 36624724 DOI: 10.1094/phyto-10-21-0430-per] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Open research practices have been highlighted extensively during the last 10 years in many fields of scientific study as essential standards needed to promote transparency and reproducibility of scientific results. Scientific claims can only be evaluated based on how protocols, materials, equipment, and methods were described; data were collected and prepared; and analyses were conducted. Openly sharing protocols, data, and computational code is central to current scholarly dissemination and communication, but in many fields, including plant pathology, adoption of these practices has been slow. We randomly selected 450 articles published from 2012 to 2021 across 21 journals representative of the plant pathology discipline and assigned them scores reflecting their openness and computational reproducibility. We found that most of the articles did not follow protocols for open science and failed to share data or code in a reproducible way. We propose that use of open-source tools facilitates computationally reproducible work and analyses, benefitting not just readers but the authors as well. Finally, we provide ideas and suggest tools to promote open, reproducible computational research practices among plant pathologists. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.
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Affiliation(s)
- Adam H Sparks
- Department of Primary Industries and Regional Development, Perth, WA 6000, Australia
- University of Southern Queensland, Centre for Crop Health, Toowoomba, Qld 4350, Australia
| | | | - Kaique S Alves
- Departmento de Fitopatologia, Universidade Federal de Viçosa, Brazil
| | - Zachary S L Foster
- Horticultural Crops Disease and Pest Management Research Unit, U.S. Department of Agriculture-Agricultural Research Service, Corvallis, OR 97330, U.S.A
| | - Niklaus J Grünwald
- Horticultural Crops Disease and Pest Management Research Unit, U.S. Department of Agriculture-Agricultural Research Service, Corvallis, OR 97330, U.S.A
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16
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Haven TL, Abunijela S, Hildebrand N. Biomedical supervisors' role modeling of open science practices. eLife 2023; 12:83484. [PMID: 37211820 DOI: 10.7554/elife.83484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 05/04/2023] [Indexed: 05/23/2023] Open
Abstract
Supervision is one important way to socialize Ph.D. candidates into open and responsible research. We hypothesized that one should be more likely to identify open science practices (here publishing open access and sharing data) in empirical publications that were part of a Ph.D. thesis when the Ph.D. candidates' supervisors engaged in these practices compared to those whose supervisors did not or less often did. Departing from thesis repositories at four Dutch University Medical centers, we included 211 pairs of supervisors and Ph.D. candidates, resulting in a sample of 2062 publications. We determined open access status using UnpaywallR and Open Data using Oddpub, where we also manually screened publications with potential open data statements. Eighty-three percent of our sample was published openly, and 9% had open data statements. Having a supervisor who published open access more often than the national average was associated with an odds of 1.99 to publish open access. However, this effect became nonsignificant when correcting for institutions. Having a supervisor who shared data was associated with 2.22 (CI:1.19-4.12) times the odds to share data compared to having a supervisor that did not. This odds ratio increased to 4.6 (CI:1.86-11.35) after removing false positives. The prevalence of open data in our sample was comparable to international studies; open access rates were higher. Whilst Ph.D. candidates spearhead initiatives to promote open science, this study adds value by investigating the role of supervisors in promoting open science.
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Affiliation(s)
- Tamarinde L Haven
- Danish Centre for Studies in Research and Research Policy, Department of Political Science, Aarhus University, Aarhus, Denmark
| | - Susan Abunijela
- QUEST Center for Responsible Research, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Nicole Hildebrand
- QUEST Center for Responsible Research, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
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17
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Li S, Litvin V, Manski CF. Partial Identification of Personalized Treatment Response with Trial-reported Analyses of Binary Subgroups. Epidemiology 2023; 34:319-324. [PMID: 36715981 DOI: 10.1097/ede.0000000000001593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Medical journals have adhered to a reporting practice that seriously limits the usefulness of published trial findings. Medical decision makers commonly observe many patient covariates and seek to use this information to personalize treatment choices. Yet standard summaries of trial findings only partition subjects into broad subgroups, typically binary categories. Given this reporting practice, we study the problem of inference on long mean treatment outcomes E[y(t)|x], where t is a treatment, y(t) is a treatment outcome, and the covariate vector x has length K, each component being a binary variable. The available data are estimates of {E[y(t)|x k = 0], E[y(t)|x k = 1], P(x k )}, k = 1,..., K reported in journal articles. We show that reported trial findings partially identify {E[y(t)|x], P(x)}. Illustrative computations demonstrate that the summaries of trial findings in journal articles may imply only wide bounds on long mean outcomes. One can realistically tighten inferences if one can combine reported trial findings with credible assumptions having identifying power, such as bounded-variation assumptions.
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Affiliation(s)
- Sheyu Li
- From the Department of Endocrinology and Metabolism, MAGIC China Centre, Cochrane China Centre, Chinese Evidence-based Medicine Centre, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Valentyn Litvin
- Department of Economics, Northwestern University, Evanston, IL 60208-2600, USA
| | - Charles F Manski
- Department of Economics, Northwestern University, Evanston, IL 60208-2600, USA
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18
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Yeung AWK, Robertson M, Uecker A, Fox PT, Eickhoff SB. Trends in the sample size, statistics, and contributions to the BrainMap database of activation likelihood estimation meta-analyses: An empirical study of 10-year data. Hum Brain Mapp 2023; 44:1876-1887. [PMID: 36479854 PMCID: PMC9980884 DOI: 10.1002/hbm.26177] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 11/18/2022] [Accepted: 11/28/2022] [Indexed: 12/13/2022] Open
Abstract
The literature of neuroimaging meta-analysis has been thriving for over a decade. A majority of them were coordinate-based meta-analyses, particularly the activation likelihood estimation (ALE) approach. A meta-evaluation of these meta-analyses was performed to qualitatively evaluate their design and reporting standards. The publications listed from the BrainMap website were screened. Six hundred and three ALE papers published during 2010-2019 were included and analysed. For reporting standards, most of the ALE papers reported their total number of Papers involved and mentioned the inclusion/exclusion criteria on Paper selection. However, most papers did not describe how data redundancy was avoided when multiple related Experiments were reported within one paper. The most prevalent repeated-measures correction methods were voxel-level FDR (54.4%) and cluster-level FWE (33.8%), with the latter quickly replacing the former since 2016. For study characteristics, sample size in terms of number of Papers included per ALE paper and number of Experiments per analysis seemed to be stable over the decade. One-fifth of the surveyed ALE papers failed to meet the recommendation of having >17 Experiments per analysis. For data sharing, most of them did not provide input and output data. In conclusion, the field has matured well in terms of rising dominance of cluster-level FWE correction, and slightly improved reporting on elimination of data redundancy and providing input data. The provision of Data and Code availability statements and flow chart of literature screening process, as well as data submission to BrainMap, should be more encouraged.
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Affiliation(s)
- Andy Wai Kan Yeung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong, China
| | - Michaela Robertson
- Research Imaging Institute, University of Texas Health Science Center, San Antonio, Texas, USA
| | - Angela Uecker
- Research Imaging Institute, University of Texas Health Science Center, San Antonio, Texas, USA
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center, San Antonio, Texas, USA.,Department of Radiology, University of Texas Health Science Center, San Antonio, Texas, USA
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
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19
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Buzbas EO, Devezer B, Baumgaertner B. The logical structure of experiments lays the foundation for a theory of reproducibility. ROYAL SOCIETY OPEN SCIENCE 2023; 10:221042. [PMID: 36938532 PMCID: PMC10014247 DOI: 10.1098/rsos.221042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 02/02/2023] [Indexed: 06/18/2023]
Abstract
The scientific reform movement has proposed openness as a potential remedy to the putative reproducibility or replication crisis. However, the conceptual relationship among openness, replication experiments and results reproducibility has been obscure. We analyse the logical structure of experiments, define the mathematical notion of idealized experiment and use this notion to advance a theory of reproducibility. Idealized experiments clearly delineate the concepts of replication and results reproducibility, and capture key differences with precision, allowing us to study the relationship among them. We show how results reproducibility varies as a function of the elements of an idealized experiment, the true data-generating mechanism, and the closeness of the replication experiment to an original experiment. We clarify how openness of experiments is related to designing informative replication experiments and to obtaining reproducible results. With formal backing and evidence, we argue that the current 'crisis' reflects inadequate attention to a theoretical understanding of results reproducibility.
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Affiliation(s)
- Erkan O. Buzbas
- Department of Mathematics and Statistical Science, University of Idaho, Moscow, ID 83844, USA
| | - Berna Devezer
- Department of Mathematics and Statistical Science, University of Idaho, Moscow, ID 83844, USA
- Department of Business, University of Idaho, Moscow, ID 83844, USA
| | - Bert Baumgaertner
- Department of Politics and Philosophy, University of Idaho, Moscow, ID 83844, USA
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20
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Lauterbach S, Dienhart H, Range J, Malzacher S, Spöring JD, Rother D, Pinto MF, Martins P, Lagerman CE, Bommarius AS, Høst AV, Woodley JM, Ngubane S, Kudanga T, Bergmann FT, Rohwer JM, Iglezakis D, Weidemann A, Wittig U, Kettner C, Swainston N, Schnell S, Pleiss J. EnzymeML: seamless data flow and modeling of enzymatic data. Nat Methods 2023; 20:400-402. [PMID: 36759590 DOI: 10.1038/s41592-022-01763-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 12/21/2022] [Indexed: 02/11/2023]
Abstract
The design of biocatalytic reaction systems is highly complex owing to the dependency of the estimated kinetic parameters on the enzyme, the reaction conditions, and the modeling method. Consequently, reproducibility of enzymatic experiments and reusability of enzymatic data are challenging. We developed the XML-based markup language EnzymeML to enable storage and exchange of enzymatic data such as reaction conditions, the time course of the substrate and the product, kinetic parameters and the kinetic model, thus making enzymatic data findable, accessible, interoperable and reusable (FAIR). The feasibility and usefulness of the EnzymeML toolbox is demonstrated in six scenarios, for which data and metadata of different enzymatic reactions are collected and analyzed. EnzymeML serves as a seamless communication channel between experimental platforms, electronic lab notebooks, tools for modeling of enzyme kinetics, publication platforms and enzymatic reaction databases. EnzymeML is open and transparent, and invites the community to contribute. All documents and codes are freely available at https://enzymeml.org .
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Affiliation(s)
- Simone Lauterbach
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Stuttgart, Germany
| | - Hannah Dienhart
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Stuttgart, Germany
| | - Jan Range
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Stuttgart, Germany
| | - Stephan Malzacher
- Institute of Bio- and Geosciences 1, Forschungszentrum Jülich, Jülich, Germany.,Aachen Biology and Biotechnology, RWTH Aachen University, Aachen, Germany
| | - Jan-Dirk Spöring
- Institute of Bio- and Geosciences 1, Forschungszentrum Jülich, Jülich, Germany.,Aachen Biology and Biotechnology, RWTH Aachen University, Aachen, Germany
| | - Dörte Rother
- Institute of Bio- and Geosciences 1, Forschungszentrum Jülich, Jülich, Germany.,Aachen Biology and Biotechnology, RWTH Aachen University, Aachen, Germany
| | - Maria Filipa Pinto
- i3S, Instituto de Investigação e Inovação em Saúde da Universidade do Porto, University of Porto, Porto, Portugal
| | - Pedro Martins
- i3S, Instituto de Investigação e Inovação em Saúde da Universidade do Porto, University of Porto, Porto, Portugal
| | - Colton E Lagerman
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Andreas S Bommarius
- School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Amalie Vang Høst
- Department of Chemical and Biochemical Engineering, Technical University of Denmark, Kgs Lyngby, Denmark
| | - John M Woodley
- Department of Chemical and Biochemical Engineering, Technical University of Denmark, Kgs Lyngby, Denmark
| | - Sandile Ngubane
- Department of Biotechnology and Food Science, Durban University of Technology, Durban, South Africa
| | - Tukayi Kudanga
- Department of Biotechnology and Food Science, Durban University of Technology, Durban, South Africa
| | | | - Johann M Rohwer
- Department of Biochemistry, Stellenbosch University, Stellenbosch, South Africa
| | - Dorothea Iglezakis
- Information and Communication Center, University of Stuttgart, Stuttgart, Germany
| | | | - Ulrike Wittig
- Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
| | | | - Neil Swainston
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Santiago Schnell
- Department of Biological Sciences and Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA
| | - Jürgen Pleiss
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Stuttgart, Germany.
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21
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Déglin SE, Burstyn I, Chen CL, Miller DJ, Gribble MO, Hamade AK, Chang ET, Avanasi R, Boon D, Reed J. Considerations towards the better integration of epidemiology into quantitative risk assessment. GLOBAL EPIDEMIOLOGY 2022; 4:100084. [PMID: 37637021 PMCID: PMC10445996 DOI: 10.1016/j.gloepi.2022.100084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 09/07/2022] [Indexed: 11/16/2022] Open
Abstract
Environmental epidemiology has proven critical to study various associations between environmental exposures and adverse human health effects. However, there is a perception that it often does not sufficiently inform quantitative risk assessment. To help address this concern, in 2017, the Health and Environmental Sciences Institute initiated a project engaging the epidemiology, exposure science, and risk assessment communities with tripartite representation from government agencies, industry, and academia, in a dialogue on the use of environmental epidemiology for quantitative risk assessment and public health decision making. As part of this project, four meetings attended by experts in epidemiology, exposure science, toxicology, statistics, and risk assessment, as well as one additional meeting engaging funding agencies, were organized to explore incentives and barriers to realizing the full potential of epidemiological data in quantitative risk assessment. A set of questions was shared with workshop participants prior to the meetings, and two case studies were used to support the discussion. Five key ideas emerged from these meetings as areas of desired improvement to ensure that human data can more consistently become an integral part of quantitative risk assessment: 1) reducing confirmation and publication bias, 2) increasing communication with funding agencies to raise awareness of research needs, 3) developing alternative funding channels targeted to support quantitative risk assessment, 4) making data available for reuse and analysis, and 5) developing cross-disciplinary and cross-sectoral interactions, collaborations, and training. We explored and integrated these themes into a roadmap illustrating the need for a multi-stakeholder effort to ensure that epidemiological data can fully contribute to the quantitative evaluation of human health risks, and to build confidence in a reliable decision-making process that leverages the totality of scientific evidence.
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Affiliation(s)
- Sandrine E. Déglin
- Health and Environmental Sciences Institute, Washington, DC, United States of America
| | - Igor Burstyn
- Department of Environmental and Occupational Health, Drexel University, Philadelphia, PA, United States of America
| | - Connie L. Chen
- Health and Environmental Sciences Institute, Washington, DC, United States of America
| | - David J. Miller
- U.S. Environmental Protection Agency, Washington, DC, United States of America
| | - Matthew O. Gribble
- Department of Epidemiology, University of Alabama at Birmingham School of Public Health, Birmingham, AL, United States of America
| | - Ali K. Hamade
- Oregon Health Authority, Portland, OR, United States of America
| | - Ellen T. Chang
- Center for Health Sciences, Exponent, Inc., Menlo Park, CA, United States of America
| | | | - Denali Boon
- Corteva Agriscience, Indianapolis, IN, United States of America
| | - Jennifer Reed
- Bayer Crop Science, Chesterfield, MO, United States of America
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22
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Chiriboga L, Callis GM, Wang Y, Chlipala E. Guide for collecting and reporting metadata on protocol variables and parameters from slide-based histotechnology assays to enhance reproducibility. J Histotechnol 2022; 45:132-147. [DOI: 10.1080/01478885.2022.2134022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Luis Chiriboga
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA
- NYULH Center for Biospecimen Research and Development, New York, NY, USA
| | | | - Yongfu Wang
- Stowers Institute for Medical Research, Kansas, MO, USA
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23
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Zavalis EA, Ioannidis JPA. A meta-epidemiological assessment of transparency indicators of infectious disease models. PLoS One 2022; 17:e0275380. [PMID: 36206207 PMCID: PMC9543956 DOI: 10.1371/journal.pone.0275380] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 09/15/2022] [Indexed: 01/04/2023] Open
Abstract
Mathematical models have become very influential, especially during the COVID-19 pandemic. Data and code sharing are indispensable for reproducing them, protocol registration may be useful sometimes, and declarations of conflicts of interest (COIs) and of funding are quintessential for transparency. Here, we evaluated these features in publications of infectious disease-related models and assessed whether there were differences before and during the COVID-19 pandemic and for COVID-19 models versus models for other diseases. We analysed all PubMed Central open access publications of infectious disease models published in 2019 and 2021 using previously validated text mining algorithms of transparency indicators. We evaluated 1338 articles: 216 from 2019 and 1122 from 2021 (of which 818 were on COVID-19); almost a six-fold increase in publications within the field. 511 (39.2%) were compartmental models, 337 (25.2%) were time series, 279 (20.9%) were spatiotemporal, 186 (13.9%) were agent-based and 25 (1.9%) contained multiple model types. 288 (21.5%) articles shared code, 332 (24.8%) shared data, 6 (0.4%) were registered, and 1197 (89.5%) and 1109 (82.9%) contained COI and funding statements, respectively. There was no major changes in transparency indicators between 2019 and 2021. COVID-19 articles were less likely to have funding statements and more likely to share code. Further validation was performed by manual assessment of 10% of the articles identified by text mining as fulfilling transparency indicators and of 10% of the articles lacking them. Correcting estimates for validation performance, 26.0% of papers shared code and 41.1% shared data. On manual assessment, 5/6 articles identified as registered had indeed been registered. Of articles containing COI and funding statements, 95.8% disclosed no conflict and 11.7% reported no funding. Transparency in infectious disease modelling is relatively low, especially for data and code sharing. This is concerning, considering the nature of this research and the heightened influence it has acquired.
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Affiliation(s)
- Emmanuel A. Zavalis
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, United States of America
- Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Solna, Stockholm, Sweden
| | - John P. A. Ioannidis
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, United States of America
- Departments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, Stanford University, Stanford, California, United States of America
- * E-mail:
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24
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Casas AI, Hassan AA, Manz Q, Wiwie C, Kleikers P, Egea J, López MG, List M, Baumbach J, Schmidt HHHW. Un-biased housekeeping gene panel selection for high-validity gene expression analysis. Sci Rep 2022; 12:12324. [PMID: 35853974 PMCID: PMC9296577 DOI: 10.1038/s41598-022-15989-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 07/04/2022] [Indexed: 12/02/2022] Open
Abstract
Differential gene expression normalised to a single housekeeping (HK) is used to identify disease mechanisms and therapeutic targets. HK gene selection is often arbitrary, potentially introducing systematic error and discordant results. Here we examine these risks in a disease model of brain hypoxia. We first identified the eight most frequently used HK genes through a systematic review. However, we observe that in both ex-vivo and in vivo, their expression levels varied considerably between conditions. When applying these genes to normalise expression levels of the validated stroke target gene, inducible Nox4, we obtained opposing results. As an alternative tool for unbiased HK gene selection, software tools exist but are limited to individual datasets lacking genome-wide search capability and user-friendly interfaces. We, therefore, developed the HouseKeepR algorithm to rapidly analyse multiple gene expression datasets in a disease-specific manner and rank HK gene candidates according to stability in an unbiased manner. Using a panel of de novo top-ranked HK genes for brain hypoxia, but not single genes, Nox4 induction was consistently reproduced. Thus, differential gene expression analysis is best normalised against a HK gene panel selected in an unbiased manner. HouseKeepR is the first user-friendly, bias-free, and broadly applicable tool to automatically propose suitable HK genes in a tissue- and disease-dependent manner.
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Affiliation(s)
- Ana I Casas
- Department of Neurology and Center for Translational Neuro- and Behavioural Sciences (C-TNBS), University Clinics Essen, Essen, Germany. .,Department of Pharmacology & Personalised Medicine, MeHNS, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands.
| | - Ahmed A Hassan
- Department of Pharmacology & Personalised Medicine, MeHNS, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Quirin Manz
- Faculty of Mathematics, Informatics and Natural Sciences, University of Hamburg, Hamburg, Germany
| | - Christian Wiwie
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Pamela Kleikers
- Department of Pharmacology & Personalised Medicine, MeHNS, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Javier Egea
- Molecular Neuroinflammation and Neuronal Plasticity Research Laboratory, Hospital Universitario Santa Cristina, Instituto de Investigación Sanitaria-Hospital Universitario de la Princesa, Madrid, Spain.,Departamento de Farmacología, Instituto de I+D del Medicamento Teófilo Hernando (ITH), Facultad de Medicina, Universidad Autónoma de Madrid, Madrid, Spain
| | - Manuela G López
- Departamento de Farmacología, Instituto de I+D del Medicamento Teófilo Hernando (ITH), Facultad de Medicina, Universidad Autónoma de Madrid, Madrid, Spain
| | - Markus List
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Jan Baumbach
- Faculty of Mathematics, Informatics and Natural Sciences, University of Hamburg, Hamburg, Germany
| | - Harald H H W Schmidt
- Department of Pharmacology & Personalised Medicine, MeHNS, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands.
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25
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Louderback ER, Gainsbury SM, Heirene RM, Amichia K, Grossman A, Bernhard BJ, LaPlante DA. Open Science Practices in Gambling Research Publications (2016-2019): A Scoping Review. J Gambl Stud 2022; 39:987-1011. [PMID: 35678905 PMCID: PMC9178323 DOI: 10.1007/s10899-022-10120-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/20/2022] [Indexed: 12/04/2022]
Abstract
The replication crisis has stimulated researchers around the world to adopt open science research practices intended to reduce publication bias and improve research quality. Open science practices include study pre-registration, open data, open access, and avoiding methods that can lead to publication bias and low replication rates. Although gambling studies uses similar research methods as behavioral research fields that have struggled with replication, we know little about the uptake of open science research practices in gambling-focused research. We conducted a scoping review of 500 recent (1/1/2016–12/1/2019) studies focused on gambling and problem gambling to examine the use of open science and transparent research practices. Our results showed that a small percentage of studies used most practices: whereas 54.6% (95% CI: [50.2, 58.9]) of studies used at least one of nine open science practices, each practice’s prevalence was: 1.6% for pre-registration (95% CI: [0.8, 3.1]), 3.2% for open data (95% CI: [2.0, 5.1]), 0% for open notebook, 35.2% for open access (95% CI: [31.1, 39.5]), 7.8% for open materials (95% CI: [5.8, 10.5]), 1.4% for open code (95% CI: [0.7, 2.9]), and 15.0% for preprint posting (95% CI: [12.1, 18.4]). In all, 6.4% (95% CI: [4.6, 8.9]) of the studies included a power analysis and 2.4% (95% CI: [1.4, 4.2]) were replication studies. Exploratory analyses showed that studies that used any open science practice, and open access in particular, had higher citation counts. We suggest several practical ways to enhance the uptake of open science principles and practices both within gambling studies and in science more generally.
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Affiliation(s)
- Eric R Louderback
- Division on Addiction, Cambridge Health Alliance, a Harvard Medical School Teaching Hospital, Malden, MA, USA
- Harvard Medical School, Boston, MA, USA
| | | | | | - Karen Amichia
- Division on Addiction, Cambridge Health Alliance, a Harvard Medical School Teaching Hospital, Malden, MA, USA
| | - Alessandra Grossman
- Division on Addiction, Cambridge Health Alliance, a Harvard Medical School Teaching Hospital, Malden, MA, USA
| | - Bo J Bernhard
- International Gaming Institute, University of Nevada, Las Vegas, NV, USA
- University of Nevada, Reno, NV, USA
| | - Debi A LaPlante
- Division on Addiction, Cambridge Health Alliance, a Harvard Medical School Teaching Hospital, Malden, MA, USA
- Harvard Medical School, Boston, MA, USA
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26
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Igumbor JO, Bosire EN, Vicente-Crespo M, Igumbor EU, Olalekan UA, Chirwa TF, Kinyanjui SM, Kyobutungi C, Fonn S. Considerations for an integrated population health databank in Africa: lessons from global best practices. Wellcome Open Res 2022; 6:214. [PMID: 35224211 PMCID: PMC8844538 DOI: 10.12688/wellcomeopenres.17000.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/12/2021] [Indexed: 12/17/2022] Open
Abstract
Background: The rising digitisation and proliferation of data sources and repositories cannot be ignored. This trend expands opportunities to integrate and share population health data. Such platforms have many benefits, including the potential to efficiently translate information arising from such data to evidence needed to address complex global health challenges. There are pockets of quality data on the continent that may benefit from greater integration. Integration of data sources is however under-explored in Africa. The aim of this article is to identify the requirements and provide practical recommendations for developing a multi-consortia public and population health data-sharing framework for Africa. Methods: We conducted a narrative review of global best practices and policies on data sharing and its optimisation. We searched eight databases for publications and undertook an iterative snowballing search of articles cited in the identified publications. The Leximancer software
© enabled content analysis and selection of a sample of the most relevant articles for detailed review. Themes were developed through immersion in the extracts of selected articles using inductive thematic analysis. We also performed interviews with public and population health stakeholders in Africa to gather their experiences, perceptions, and expectations of data sharing. Results: Our findings described global stakeholder experiences on research data sharing. We identified some challenges and measures to harness available resources and incentivise data sharing. We further highlight progress made by the different groups in Africa and identified the infrastructural requirements and considerations when implementing data sharing platforms. Furthermore, the review suggests key reforms required, particularly in the areas of consenting, privacy protection, data ownership, governance, and data access. Conclusions: The findings underscore the critical role of inclusion, social justice, public good, data security, accountability, legislation, reciprocity, and mutual respect in developing a responsive, ethical, durable, and integrated research data sharing ecosystem.
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Affiliation(s)
- Jude O Igumbor
- School of Public Health, University of the Witwatersrand, Johannesburg, Gauteng, 2193, South Africa
| | - Edna N Bosire
- School of Public Health, University of the Witwatersrand, Johannesburg, Gauteng, 2193, South Africa
| | - Marta Vicente-Crespo
- School of Public Health, University of the Witwatersrand, Johannesburg, Gauteng, 2193, South Africa.,African Population and Health Research Centre, Nairobi, Kenya
| | - Ehimario U Igumbor
- Nigeria Centre for Disease Control, Abuja, Nigeria.,School of Public Health, University of the Western Cape, Cape Town, Western Cape, South Africa
| | - Uthman A Olalekan
- Warwick-Centre for Applied Health Research and Delivery (WCAHRD), Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | - Tobias F Chirwa
- School of Public Health, University of the Witwatersrand, Johannesburg, Gauteng, 2193, South Africa
| | | | | | - Sharon Fonn
- School of Public Health, University of the Witwatersrand, Johannesburg, Gauteng, 2193, South Africa
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27
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Transparent and Reproducible Research Practices in the Surgical Literature. J Surg Res 2022; 274:116-124. [PMID: 35150944 DOI: 10.1016/j.jss.2021.09.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 08/23/2021] [Accepted: 09/20/2021] [Indexed: 11/23/2022]
Abstract
INTRODUCTION Previous studies have established a baseline of minimal reproducibility in the social science and biomedical literature. Clinical research is especially deficient in factors of reproducibility. Surgical journals contain fewer clinical trials than non-surgical areas of medicine, suggesting that it should be easier to reproduce the outcomes of surgical literature. METHODS In this study, we evaluated a broad range of indicators related to transparency and reproducibility in a random sample of 387 articles published in Surgery journals between 2014 and 2018. RESULTS A small minority of our sample made available their materials (5.3%, 95% C.I. 2.4%-8.2%), protocols (1.2%, 0-2.5%), data (2.5%, 0.7%-4.2%), or analysis scripts (0.04%). Four studies were adequately pre-registered. No studies were explicit replications of previous literature. Most studies (58%), declined to provide a funding statement, while conflicts of interest were declared in a small fraction (9.3%). Most have not been cited by systematic reviews (83%) or meta-analyses (87%), and most were only accessible to paying subscribers (59%). CONCLUSIONS The transparency of the surgical literature could improve with adherence to baseline standards of reproducibility.
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28
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Thibault RT, Munafò MR, Moher D. Rigour and reproducibility in Canadian research: call for a coordinated approach. Facets (Ott) 2022. [DOI: 10.1139/facets-2021-0162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Shortcomings in the rigour and reproducibility of research have become well-known issues and persist despite repeated calls for improvement. A coordinated effort among researchers, institutions, funders, publishers, learned societies, and regulators may be the most effective way of tackling these issues. The UK Reproducibility Network (UKRN) has fostered collaboration across various stakeholders in research and are creating the infrastructure necessary to advance rigorous and reproducible research practices across the United Kingdom. Other Reproducibility Networks, modelled on UKRN, are now emerging in other countries. Canada could benefit from a comparable network to unify the voices around research quality and maximize the value of Canadian research.
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Affiliation(s)
- Robert T. Thibault
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, California, 94305, United States
- School of Psychological Science, University of Bristol, Bristol, BS8 1TH, United Kingdom
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, BS8 1TH, United Kingdom
| | - Marcus R. Munafò
- School of Psychological Science, University of Bristol, Bristol, BS8 1TH, United Kingdom
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, BS8 1TH, United Kingdom
| | - David Moher
- Centre for Journalology, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, K1Y 4E9, Canada
- School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, ON, K1N 6N5, Canada
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29
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Shukla R, Yadav AK, Sote WO, Junior MC, Singh TR. Systems biology and big data analytics. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00005-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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30
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Hardwicke TE, Thibault RT, Kosie JE, Wallach JD, Kidwell MC, Ioannidis JPA. Estimating the Prevalence of Transparency and Reproducibility-Related Research Practices in Psychology (2014-2017). PERSPECTIVES ON PSYCHOLOGICAL SCIENCE 2022; 17:239-251. [PMID: 33682488 PMCID: PMC8785283 DOI: 10.1177/1745691620979806] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Psychologists are navigating an unprecedented period of introspection about the credibility and utility of their discipline. Reform initiatives emphasize the benefits of transparency and reproducibility-related research practices; however, adoption across the psychology literature is unknown. Estimating the prevalence of such practices will help to gauge the collective impact of reform initiatives, track progress over time, and calibrate future efforts. To this end, we manually examined a random sample of 250 psychology articles published between 2014 and 2017. Over half of the articles were publicly available (154/237, 65%, 95% confidence interval [CI] = [59%, 71%]); however, sharing of research materials (26/183; 14%, 95% CI = [10%, 19%]), study protocols (0/188; 0%, 95% CI = [0%, 1%]), raw data (4/188; 2%, 95% CI = [1%, 4%]), and analysis scripts (1/188; 1%, 95% CI = [0%, 1%]) was rare. Preregistration was also uncommon (5/188; 3%, 95% CI = [1%, 5%]). Many articles included a funding disclosure statement (142/228; 62%, 95% CI = [56%, 69%]), but conflict-of-interest statements were less common (88/228; 39%, 95% CI = [32%, 45%]). Replication studies were rare (10/188; 5%, 95% CI = [3%, 8%]), and few studies were included in systematic reviews (21/183; 11%, 95% CI = [8%, 16%]) or meta-analyses (12/183; 7%, 95% CI = [4%, 10%]). Overall, the results suggest that transparency and reproducibility-related research practices were far from routine. These findings establish baseline prevalence estimates against which future progress toward increasing the credibility and utility of psychology research can be compared.
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Affiliation(s)
- Tom E. Hardwicke
- Department of Psychology, University of Amsterdam
- Meta-Research Innovation Center Berlin (METRIC-B), QUEST Center for Transforming Biomedical Research, Berlin Institute of Health, Charité–Universitätsmedizin Berlin
| | - Robert T. Thibault
- School of Psychological Science, University of Bristol
- MRC Integrative Epidemiology Unit at the University of Bristol
| | | | - Joshua D. Wallach
- Department of Environmental Health Sciences, Yale School of Public Health
| | | | - John P. A. Ioannidis
- Meta-Research Innovation Center Berlin (METRIC-B), QUEST Center for Transforming Biomedical Research, Berlin Institute of Health, Charité–Universitätsmedizin Berlin
- Department of Medicine, Stanford University
- Meta-Research Innovation Center at Stanford, Stanford University
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31
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Varangot-Reille C, Suso-Martí L, Romero-Palau M, Suárez-Pastor P, Cuenca-Martínez F. Effects of Different Therapeutic Exercise Modalities on Migraine or Tension-Type Headache: A Systematic Review and Meta-Analysis with a Replicability Analysis. THE JOURNAL OF PAIN 2021; 23:1099-1122. [PMID: 34929374 DOI: 10.1016/j.jpain.2021.12.003] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 09/09/2021] [Accepted: 12/15/2021] [Indexed: 12/11/2022]
Abstract
The primary aim of this study was to review the effect of exercise in comparison with a non-active treatment on pain intensity, frequency of headache episodes, headache duration, quality of life, medication use, and psychological symptoms, in patients with migraine or tension-type headache (TTH). A systematic search was conducted in various electronic databases to identify all relevant studies: Medline (PubMed), PEDro, EBSCO and Google Scholar. Clinical trials assessing the effects of exercise interventions in patients with primary headaches were selected. Methodological quality was evaluated using the Cochrane Risk of Bias Tool and PEDro scale and qualitative analysis was based on classifying the results into levels of evidence according to the GRADE. 19 studies (2776 participants; 85% female) were included. The meta-analysis showed statistically significant differences in pain intensity for aerobic training in patients with migraine (SMD = -0.65; 95% CI = -1.07 to -0.22, very low certainty evidence) and for strength training in patients with TTH (SMD = -0.84; 95% CI = -1.68 to- -0.01, very low certainty evidence). Statistically significant differences were also found in the medication use (SMD = -0.51; 95% CI = -0.85 to -0.17, low certainty evidence). Low transparency, replicability and high risk of bias were found. Aerobic training has a small to moderate clinical effect on pain intensity and medication use on migraine patients, with very low to low certainty of evidence. Strength training showed a moderate clinical effect with very low quality of evidence in patients with TTH. Exercise could be considered as clinically relevant for the management of patients with primary headaches, but the presence of low certainty of evidence and low transparency and replicability limited its clinical application. PERSPECTIVE: This article presents current evidence about exercise interventions in patients with primary headaches, including migraine and tension-type headache. Existing findings are reviewed, and relevant data are provided on the effectiveness of each exercise modality, as well as its certainty of evidence and clinical applicability.
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Affiliation(s)
- Clovis Varangot-Reille
- Exercise Intervention for Health Research Group (EXINH-RG), Department of Physiotherapy, University of Valencia, Valencia, Spain
| | - Luis Suso-Martí
- Exercise Intervention for Health Research Group (EXINH-RG), Department of Physiotherapy, University of Valencia, Valencia, Spain.
| | | | - Pablo Suárez-Pastor
- Deparment of Physiotherapy, Centro Superior de Estudios Universitarios La Salle, Universidad Autónoma de Madrid, Madrid, Spain
| | - Ferran Cuenca-Martínez
- Exercise Intervention for Health Research Group (EXINH-RG), Department of Physiotherapy, University of Valencia, Valencia, Spain
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32
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Meid AD. Teaching reproducible research for medical students and postgraduate pharmaceutical scientists. BMC Res Notes 2021; 14:445. [PMID: 34886890 PMCID: PMC8656016 DOI: 10.1186/s13104-021-05862-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 11/26/2021] [Indexed: 11/10/2022] Open
Abstract
In medicine and other academic settings, (doctoral) students often work in interdisciplinary teams together with researchers of pharmaceutical sciences, natural sciences in general, or biostatistics. They should be fundamentally taught good research practices, especially in terms of statistical analysis. This includes reproducibility as a central aspect. Acknowledging that even experienced researchers and supervisors might be unfamiliar with necessary aspects of a perfectly reproducible workflow, a lecture series on reproducible research (RR) was developed for young scientists in clinical pharmacology. The pilot series highlighted definitions of RR, reasons for RR, potential merits of RR, and ways to work accordingly. In trying to actually reproduce a published analysis, several practical obstacles arose. In this article, reproduction of a working example is commented to emphasize the manifold facets of RR, to provide possible explanations for difficulties and solutions, and to argue that harmonized curricula for (quantitative) clinical researchers should include RR principles. These experiences should raise awareness among educators and students, supervisors and young scientists. RR working habits are not only beneficial for ourselves or our students, but also for other researchers within an institution, for scientific partners, for the scientific community, and eventually for the public profiting from research findings.
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Affiliation(s)
- Andreas D Meid
- Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Im Neuenheimer Feld 410, 69120, Heidelberg, Germany.
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33
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Cross S, Rho Y, Reddy H, Pepperrell T, Rodgers F, Osborne R, Eni-Olotu A, Banerjee R, Wimmer S, Keestra S. Who funded the research behind the Oxford-AstraZeneca COVID-19 vaccine? BMJ Glob Health 2021; 6:e007321. [PMID: 34937701 PMCID: PMC8704023 DOI: 10.1136/bmjgh-2021-007321] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 11/17/2021] [Indexed: 11/03/2022] Open
Abstract
OBJECTIVES The Oxford-AstraZeneca COVID-19 vaccine (ChAdOx1 nCoV-19, Vaxzevira or Covishield) builds on two decades of research and development (R&D) into chimpanzee adenovirus-vectored vaccine (ChAdOx) technology at the University of Oxford. This study aimed to approximate the funding for the R&D of ChAdOx and the Oxford-AstraZeneca vaccine and to assess the transparency of funding reporting mechanisms. METHODS We conducted a scoping review and publication history analysis of the principal investigators to reconstruct R&D funding the ChAdOx technology. We matched award numbers with publicly accessible grant databases. We filed freedom of information (FOI) requests to the University of Oxford for the disclosure of all grants for ChAdOx R&D. RESULTS We identified 100 peer-reviewed articles relevant to ChAdOx technology published between January 2002 and October 2020, extracting 577 mentions of funding bodies from acknowledgements. Government funders from overseas (including the European Union) were mentioned 158 times (27.4%), the UK government 147 (25.5%) and charitable funders 138 (23.9%). Grant award numbers were identified for 215 (37.3%) mentions; amounts were publicly available for 121 (21.0%). Based on the FOIs, until December 2019, the biggest funders of ChAdOx R&D were the European Commission (34.0%), Wellcome Trust (20.4%) and Coalition for Epidemic Preparedness Innovations (17.5%). Since January 2020, the UK government contributed 95.5% of funding identified. The total identified R&D funding was £104 226 076 reported in the FOIs and £228 466 771 reconstructed from the literature search. CONCLUSION Our study approximates that public and charitable financing accounted for 97%-99% of identifiable funding for the ChAdOx vaccine technology research at the University of Oxford underlying the Oxford-AstraZeneca vaccine until autumn 2020. We encountered a lack of transparency in research funding reporting.
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Affiliation(s)
- Samuel Cross
- Faculty of Medicine, Imperial College London, London, UK
| | | | - Henna Reddy
- Medical Sciences Division, University of Oxford, Oxford, UK
| | - Toby Pepperrell
- School of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK
| | - Florence Rodgers
- Royal Cornwall Hospital, Royal Cornwall Hospitals NHS Trust, Truro, UK
| | - Rhiannon Osborne
- University of Cambridge School of Clinical Medicine, Cambridge, UK
| | | | - Rishi Banerjee
- Manchester University NHS Foundation Trust, Manchester, UK
| | - Sabrina Wimmer
- Manchester University NHS Foundation Trust, Manchester, UK
- Department of Management, London School of Economics and Political Science, London, UK
| | - Sarai Keestra
- Epidemiology and Data Science, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
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Ávila DL, Nunes NAM, Almeida PHRF, Gomes JAS, Rosa COB, Alvarez-Leite JI. Signaling Targets Related to Antiobesity Effects of Capsaicin: A Scoping Review. Adv Nutr 2021; 12:2232-2243. [PMID: 34171094 PMCID: PMC8634413 DOI: 10.1093/advances/nmab064] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 02/22/2021] [Accepted: 05/03/2021] [Indexed: 01/01/2023] Open
Abstract
The search for new antiobesogenic agents is increasing because of the current obesity pandemic. Capsaicin (Caps), an exogenous agonist of the vanilloid receptor of transient potential type 1 (TRPV1), has shown promising results in the treatment of obesity. This scoping review aims to verify the pathways mediating the effects of Caps in obesity and the different methods adopted to identify these pathways. The search was carried out using data from the EMBASE, MEDLINE (PubMed), Web of Science, and SCOPUS databases. Studies considered eligible evaluated the mechanisms of action of Caps in obesity models or cell types involved in obesity. Nine studies were included and 100% (n = 6) of the in vivo studies showed a high risk of bias. Of the 9 studies, 66.6% (n = 6) administered Caps orally in the diet and 55.5% (n = 5) used a concentration of Caps of 0.01% in the diet. In vitro, the most tested concentration was 1 μM (88.9%; n = 8). Capsazepine was the antagonist chosen by 66.6% (n = 6) of the studies. Seven studies (77.8%) linked the antiobesogenic effects of Caps to TRPV1 activation and 3 (33.3%) indicated peroxisome proliferator-activated receptor (PPAR) involvement as an upstream connection to TRPV1, rather than a direct metabolic target of Caps. The main secondary effects of Caps were lower weight gain (33.3%; n = 3) or loss (22.2%; n = 2), greater improvement in lipid profile (33.3%; n = 3), lower white adipocyte adipogenesis (33.3%; n = 3), browning process activation (44.4%; n = 4), and higher brown adipocyte activity (33.3%; n = 3) compared with those of the control treatment. Some studies have shown that PPAR agonists modulate TRPV1 activity, and no study has evaluated the simultaneous antagonism of these 2 receptors. Consequently, further studies are necessary to elucidate the role of each of these signaling molecules in the antiobesogenic effects of Caps.
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Affiliation(s)
- Danielle L Ávila
- Instituto de Ciências Biológicas, Departamento de Bioquímica e Imunologia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Núbia A M Nunes
- Instituto de Ciências Biológicas, Departamento de Bioquímica e Imunologia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Paulo H R F Almeida
- Programa de Pós-Graduação em Medicamentos e Assistência Farmacêutica, Departamento de Farmácia Social, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Juliana A S Gomes
- Instituto de Ciências Biológicas, Departamento de Morfologia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Carla O B Rosa
- Faculdade de Nutrição, Departamento de Nutrição e Saúde, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil
| | - Jacqueline I Alvarez-Leite
- Instituto de Ciências Biológicas, Departamento de Bioquímica e Imunologia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
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Paez A. Reproducibility of Research During COVID-19: Examining the Case of Population Density and the Basic Reproductive Rate from the Perspective of Spatial Analysis. GEOGRAPHICAL ANALYSIS 2021; 54:GEAN12307. [PMID: 34898693 PMCID: PMC8652856 DOI: 10.1111/gean.12307] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 09/15/2021] [Accepted: 09/28/2021] [Indexed: 06/14/2023]
Abstract
The emergence of the novel SARS-CoV-2 coronavirus and the global COVID-19 pandemic in 2019 led to explosive growth in scientific research. Alas, much of the research in the literature lacks conditions to be reproducible, and recent publications on the association between population density and the basic reproductive number of SARS-CoV-2 are no exception. Relatively few papers share code and data sufficiently, which hinders not only verification but additional experimentation. In this article, an example of reproducible research shows the potential of spatial analysis for epidemiology research during COVID-19. Transparency and openness means that independent researchers can, with only modest efforts, verify findings and use different approaches as appropriate. Given the high stakes of the situation, it is essential that scientific findings, on which good policy depends, are as robust as possible; as the empirical example shows, reproducibility is one of the keys to ensure this.
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Affiliation(s)
- Antonio Paez
- School of EarthEnvironment and SocietyMcMaster UniversityHamiltonCanada
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Pennington CR, Jones A, Bartlett JE, Copeland A, Shaw DJ. Raising the bar: improving methodological rigour in cognitive alcohol research. Addiction 2021; 116:3243-3251. [PMID: 33999479 DOI: 10.1111/add.15563] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 01/29/2021] [Accepted: 04/28/2021] [Indexed: 01/31/2023]
Abstract
BACKGROUND AND AIMS A range of experimental paradigms claim to measure the cognitive processes underpinning alcohol use, suggesting that heightened attentional bias, greater approach tendencies and reduced cue-specific inhibitory control are important drivers of consumption. This paper identifies methodological shortcomings within this broad domain of research and exemplifies them in studies focused specifically on alcohol-related attentional bias. ARGUMENT AND ANALYSIS We highlight five main methodological issues: (i) the use of inappropriately matched control stimuli; (ii) opacity of stimulus selection and validation procedures; (iii) a credence in noisy measures; (iv) a reliance on unreliable tasks; and (v) variability in design and analysis. This is evidenced through a review of alcohol-related attentional bias (64 empirical articles, 68 tasks), which reveals the following: only 53% of tasks use appropriately matched control stimuli; as few as 38% report their stimulus selection and 19% their validation procedures; less than 28% used indices capable of disambiguating attentional processes; 22% assess reliability; and under 2% of studies were pre-registered. CONCLUSIONS Well-matched and validated experimental stimuli, the development of reliable cognitive tasks and explicit assessment of their psychometric properties, and careful consideration of behavioural indices and their analysis will improve the methodological rigour of cognitive alcohol research. Open science principles can facilitate replication and reproducibility in alcohol research.
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Affiliation(s)
| | - Andrew Jones
- Institute of Population Health Sciences, University of Liverpool, Liverpool, UK
| | | | - Amber Copeland
- Department of Psychology, University of Sheffield, Sheffield, UK
| | - Daniel J Shaw
- School of Psychology, Aston University, Birmingham, UK
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Hamilton DG, Fraser H, Fidler F, McDonald S, Rowhani-Farid A, Hong K, Page MJ. Rates and predictors of data and code sharing in the medical and health sciences: Protocol for a systematic review and individual participant data meta-analysis. F1000Res 2021; 10:491. [PMID: 34631024 PMCID: PMC8485098 DOI: 10.12688/f1000research.53874.2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/01/2021] [Indexed: 01/06/2023] Open
Abstract
Numerous studies have demonstrated low but increasing rates of data and code sharing within medical and health research disciplines. However, it remains unclear how commonly data and code are shared across all fields of medical and health research, as well as whether sharing rates are positively associated with implementation of progressive policies by publishers and funders, or growing expectations from the medical and health research community at large. Therefore this systematic review aims to synthesise the findings of medical and health science studies that have empirically investigated the prevalence of data or code sharing, or both. Objectives include the investigation of: (i) the prevalence of public sharing of research data and code alongside published articles (including preprints), (ii) the prevalence of private sharing of research data and code in response to reasonable requests, and (iii) factors associated with the sharing of either research output (e.g., the year published, the publisher's policy on sharing, the presence of a data or code availability statement). It is hoped that the results will provide some insight into how often research data and code are shared publicly and privately, how this has changed over time, and how effective some measures such as the institution of data sharing policies and data availability statements have been in motivating researchers to share their underlying data and code.
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Affiliation(s)
- Daniel G Hamilton
- MetaMelb Research Group, School of BioSciences, University of Melbourne, Parkville, Victoria, 3010, Australia
| | - Hannah Fraser
- MetaMelb Research Group, School of BioSciences, University of Melbourne, Parkville, Victoria, 3010, Australia
| | - Fiona Fidler
- MetaMelb Research Group, School of BioSciences, University of Melbourne, Parkville, Victoria, 3010, Australia.,School of Historical and Philosophical Studies, University of Melbourne, Parkville, Victoria, 3010, Australia
| | - Steve McDonald
- School of Public Health & Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia
| | - Anisa Rowhani-Farid
- Department of Pharmaceutical Health Services Research, University of Maryland, Baltimore, Maryland, 21201, USA
| | - Kyungwan Hong
- Department of Pharmaceutical Health Services Research, University of Maryland, Baltimore, Maryland, 21201, USA
| | - Matthew J Page
- School of Public Health & Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia
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Kourou K, Exarchos KP, Papaloukas C, Sakaloglou P, Exarchos T, Fotiadis DI. Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis. Comput Struct Biotechnol J 2021; 19:5546-5555. [PMID: 34712399 PMCID: PMC8523813 DOI: 10.1016/j.csbj.2021.10.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 10/04/2021] [Accepted: 10/04/2021] [Indexed: 02/08/2023] Open
Abstract
Artificial Intelligence (AI) has recently altered the landscape of cancer research and medical oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep Learning (DL) architectures. In this review article we focus on the ML aspect of AI applications in cancer research and present the most indicative studies with respect to the ML algorithms and data used. The PubMed and dblp databases were considered to obtain the most relevant research works of the last five years. Based on a comparison of the proposed studies and their research clinical outcomes concerning the medical ML application in cancer research, three main clinical scenarios were identified. We give an overview of the well-known DL and Reinforcement Learning (RL) methodologies, as well as their application in clinical practice, and we briefly discuss Systems Biology in cancer research. We also provide a thorough examination of the clinical scenarios with respect to disease diagnosis, patient classification and cancer prognosis and survival. The most relevant studies identified in the preceding year are presented along with their primary findings. Furthermore, we examine the effective implementation and the main points that need to be addressed in the direction of robustness, explainability and transparency of predictive models. Finally, we summarize the most recent advances in the field of AI/ML applications in cancer research and medical oncology, as well as some of the challenges and open issues that need to be addressed before data-driven models can be implemented in healthcare systems to assist physicians in their daily practice.
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Affiliation(s)
- Konstantina Kourou
- Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
- Foundation for Research and Technology-Hellas, Institute of Molecular Biology and Biotechnology, Dept. of Biomedical Research, Ioannina GR45110, Greece
| | | | - Costas Papaloukas
- Dept. of Biological Applications and Technology, University of Ioannina, Ioannina, Greece
| | - Prodromos Sakaloglou
- Dept. of Precision and Molecular Medicine, Unit of Liquid Biopsy in Oncology, Ioannina University Hospital, Ioannina, Greece
- Laboratory of Medical Genetics in Clinical Practice, School of Health Sciences, Faculty of Medicine, University of Ioannina, Ioannina, Greece
| | | | - Dimitrios I. Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece
- Foundation for Research and Technology-Hellas, Institute of Molecular Biology and Biotechnology, Dept. of Biomedical Research, Ioannina GR45110, Greece
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Randomization, blinding, data handling and sample size estimation in papers published in Veterinary Anaesthesia and Analgesia in 2009 and 2019. Vet Anaesth Analg 2021; 49:18-25. [PMID: 34696985 DOI: 10.1016/j.vaa.2021.09.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 09/17/2021] [Accepted: 09/17/2021] [Indexed: 11/22/2022]
Abstract
OBJECTIVE To evaluate reporting of items indicative of bias and weak study design. STUDY DESIGN Literature survey. POPULATION Papers published in Veterinary Anaesthesia and Analgesia. METHODS Reporting of randomization, blinding, sample size estimation and data exclusion were compared for papers published separated by a 10 year interval. A reporting rate of more than 95% was considered ideal. The availability of data supporting results in a publicly accessible repository was also assessed. Selected papers were randomized and identifiers removed for review, with data from 59 (57 in 2009, two in 2008) and 56 (52 in 2019, four in 2018) papers analyzed. Items were categorized for completeness of reporting using a previously published operationalized checklist. Two reviewers reviewed all papers independently. RESULTS Full reporting of randomization increased over time from 13.6% to 85.7% [95% confidence interval (CI), 57.8-86.6%; p < 0.0001], as did sample size estimation (from 0% to 20%; 95% CI, 7.6-32.4%; p = 0.002). Reporting of blinding (49.2% and 50.0%; 95% CI, -18.3% to 20.0%; p = 1.0) and exclusions of samples/animals (39.0% and 50.0%; 95% CI, -8.8% to 30.8%; p = 0.3) did not change significantly. Data availability was low (2008/2009, zero papers; 2018/2019, two papers). None of the items studied exceeded the predetermined ideal reporting rate. CONCLUSIONS AND CLINICAL RELEVANCE These results indicate that reporting quality remains low, with a risk of bias.
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Ma L, Peterson EA, Shin IJ, Muesse J, Marino K, Steliga MA, Johann DJ. NPARS-A Novel Approach to Address Accuracy and Reproducibility in Genomic Data Science. Front Big Data 2021; 4:725095. [PMID: 34647017 PMCID: PMC8503682 DOI: 10.3389/fdata.2021.725095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 09/07/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Accuracy and reproducibility are vital in science and presents a significant challenge in the emerging discipline of data science, especially when the data are scientifically complex and massive in size. Further complicating matters, in the field of genomic-based science high-throughput sequencing technologies generate considerable amounts of data that needs to be stored, manipulated, and analyzed using a plethora of software tools. Researchers are rarely able to reproduce published genomic studies. Results: Presented is a novel approach which facilitates accuracy and reproducibility for large genomic research data sets. All data needed is loaded into a portable local database, which serves as an interface for well-known software frameworks. These include python-based Jupyter Notebooks and the use of RStudio projects and R markdown. All software is encapsulated using Docker containers and managed by Git, simplifying software configuration management. Conclusion: Accuracy and reproducibility in science is of a paramount importance. For the biomedical sciences, advances in high throughput technologies, molecular biology and quantitative methods are providing unprecedented insights into disease mechanisms. With these insights come the associated challenge of scientific data that is complex and massive in size. This makes collaboration, verification, validation, and reproducibility of findings difficult. To address these challenges the NGS post-pipeline accuracy and reproducibility system (NPARS) was developed. NPARS is a robust software infrastructure and methodology that can encapsulate data, code, and reporting for large genomic studies. This paper demonstrates the successful use of NPARS on large and complex genomic data sets across different computational platforms.
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Affiliation(s)
- Li Ma
- Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, United States
- Department of Information Science, University of Arkansas at Little Rock, Little Rock, AR, United States
| | - Erich A. Peterson
- Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Ik Jae Shin
- Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Jason Muesse
- Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Katy Marino
- Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Matthew A. Steliga
- Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Donald J. Johann
- Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, United States
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Obiora OL, Olivier B, Shead DA, Withers A. Data sharing practices of health researchers in Africa: a scoping review protocol. JBI Evid Synth 2021; 20:681-688. [PMID: 34494610 DOI: 10.11124/jbies-20-00502] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
Abstract
OBJECTIVE The aim of the review is to map the existing evidence regarding the data-sharing practices of health researchers in African countries. This review will also identify perceptions; barriers; facilitators; ethical-, legal-, and author-reported recommendations; institutional- and funding-related aspects that are being considered by African health researchers on data sharing in Africa and, as a result, identify areas for development and improvement in health care on the continent. INTRODUCTION The sharing of health-related data has been widely discussed in the literature. However, sharing health-related data has yet to become a common practice among health researchers in Africa, which bears a large burden of the global health diseases. The sharing of health research data could lead to greater development and improvement in health care in Africa. INCLUSION CRITERIA This review will incorporate studies that report on data sharing among health researchers in Africa. All primary, secondary, and gray literature that report on the practice of data sharing among health researchers in Africa will be included. Studies on data sharing on topics other than health-related data will be excluded. No language restrictions will be applied. METHODS The JBI scoping review methodological framework will be adopted. An initial search of databases such as MEDLINE (PubMed), Scopus, LILAC, and Web of Science will be conducted. All search results will be screened and relevant data extracted by two independent reviewers. Data extracted will be exported into JBI SUMARI. The findings will be presented in the final scoping review report and illustrated in a PRISMA flow diagram.
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Affiliation(s)
- Oluchukwu Loveth Obiora
- Department of Physiotherapy, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa The Wits-JBI Centre for Evidenced-based Practice: A JBI Affiliated Group, Johannesburg, South Africa Department of Anatomy, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa Department of Paediatric Surgery, Chris Hani Baragwanath Academic Hospital, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
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Zhao Z, Sacks DB. Call for Action: Journals Need to Insist on Full Reporting of the Analytical Characteristics of Biomarkers. Lab Med 2021; 52:7-9. [PMID: 33258475 DOI: 10.1093/labmed/lmaa097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Zhen Zhao
- Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York
| | - David B Sacks
- Department of Laboratory Medicine, National Institutes of Health, Bethesda, Maryland
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Vihinen M. Measuring and interpreting pervasive heterogeneity, poikilosis. FASEB Bioadv 2021; 3:611-625. [PMID: 34377957 PMCID: PMC8332472 DOI: 10.1096/fba.2021-00015] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 03/09/2021] [Accepted: 03/12/2021] [Indexed: 11/11/2022] Open
Abstract
Measurements are widely used in science, engineering, industry, and trade. They form the basis for experimental scientific research, approach, and progress; however, their foundations are seldom thought or questioned. Recently poikilosis, pervasive heterogeneity ranging from subatomic level to biosphere, was introduced. Poikilosis makes single point measurements and estimates obsolete and irrelevant as measurands display intervals of magnitudes. Consideration of poikilosis requires new lines of thinking in experimental design, conduction of studies, data analysis and interpretation. Measurements of poikilosis must consider lagom, normal, variation extent. Measurements, measures, and measurands as well as the measuring systems and uncertainties are discussed from the perspective of poikilosis. New systematics is introduced for description of uncertainty in measurements and for types of experimental designs. Poikilosis-aware experimenting, data analysis and interpretation are discussed. Instructions are provided for how to measure lagom and non-lagom effects of poikilosis. Consideration of poikilosis can solve scientific controversies and enigmas and can allow novel insight into systems, processes, mechanisms, and reactions and their interpretation, understanding, and manipulation. Furthermore, it will increase reproducibility of measurements and studies.
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Affiliation(s)
- Mauno Vihinen
- Department of Experimental Medical ScienceLund UniversityLundSweden
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44
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Preregistration and Registered Reports: A Key Pathway to Enhancing Robustness and Replicability in Mental Health Research. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2021; 1:80-82. [DOI: 10.1016/j.bpsgos.2021.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 07/08/2021] [Accepted: 07/12/2021] [Indexed: 11/21/2022] Open
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Khouri C, Revol B, Lepelley M, Mouffak A, Bernardeau C, Salvo F, Pariente A, Roustit M, Cracowski JL. A meta-epidemiological study found lack of transparency and poor reporting of disproportionality analyses for signal detection in pharmacovigilance databases. J Clin Epidemiol 2021; 139:191-198. [PMID: 34329725 DOI: 10.1016/j.jclinepi.2021.07.014] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 07/08/2021] [Accepted: 07/22/2021] [Indexed: 12/18/2022]
Abstract
OBJECTIVE To review and appraise methods and reporting characteristics of pharmacovigilance disproportionality analyses. STUDY DESIGN AND SETTING We randomly selected 100 disproportionality analyses indexed in Medline found during a systematic literature search. We then extracted and synthetized methodological and reporting characteristics using 7 key items: 1) title transparency; 2) protocol pre-registration; 3) date of data extraction and analysis; 4) outcome, population, exposure and comparator definitions; 5) adjustment and stratification of results; 6) method and threshold for signal detection; 7) secondary and sensitivity analyses. RESULTS We found that methods used to generate disproportionality signals were extremely heterogeneous; there were nearly as many unique analyses as studies. The authors used various populations, methods, signal detection thresholds, adjustment or stratification variables, generally without justification for their choice or pre-specification in protocols. Moreover, 78% of studies failed to report methods for case, adverse drug reactions or comparator selection and 32 studies did not define the threshold for signal generation. CONCLUSION Our survey raises major concerns regarding all aspects of disproportionality analyses that could lead to misleading results and generate unjustified alarms. We advocate for a strong and transparent rationale for variable selection, choice of population and comparators pre-specified in a protocol and assessed by sensitivity analyses.
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Affiliation(s)
- Charles Khouri
- Pharmacovigilance Unit, Grenoble Alpes University Hospital, F-38000 Grenoble, France; Clinical Pharmacology Department INSERM CIC 1406, Grenoble Alpes University Hospital, F-38000 Grenoble, France; HP2 Laboratory, INSERM U1300, Univ. Grenoble Alpes, F-38000 Grenoble, France..
| | - Bruno Revol
- Pharmacovigilance Unit, Grenoble Alpes University Hospital, F-38000 Grenoble, France; HP2 Laboratory, INSERM U1300, Univ. Grenoble Alpes, F-38000 Grenoble, France
| | - Marion Lepelley
- Pharmacovigilance Unit, Grenoble Alpes University Hospital, F-38000 Grenoble, France
| | - Amelle Mouffak
- Pharmacovigilance Unit, Grenoble Alpes University Hospital, F-38000 Grenoble, France
| | - Claire Bernardeau
- Pharmacovigilance Unit, Grenoble Alpes University Hospital, F-38000 Grenoble, France
| | - Francesco Salvo
- INSERM U1219, Bordeaux Population Health, Team Pharmacoepidemiology, University of Bordeaux, F-33000 Bordeaux, France.; Medical Pharmacology Unit, Public Health Division, Bordeaux University Hospital (CHU), 33000 Bordeaux, France
| | - Antoine Pariente
- INSERM U1219, Bordeaux Population Health, Team Pharmacoepidemiology, University of Bordeaux, F-33000 Bordeaux, France.; Medical Pharmacology Unit, Public Health Division, Bordeaux University Hospital (CHU), 33000 Bordeaux, France
| | - Matthieu Roustit
- Clinical Pharmacology Department INSERM CIC 1406, Grenoble Alpes University Hospital, F-38000 Grenoble, France; HP2 Laboratory, INSERM U1300, Univ. Grenoble Alpes, F-38000 Grenoble, France
| | - Jean-Luc Cracowski
- Pharmacovigilance Unit, Grenoble Alpes University Hospital, F-38000 Grenoble, France; HP2 Laboratory, INSERM U1300, Univ. Grenoble Alpes, F-38000 Grenoble, France
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Johnson BS, Rauh S, Tritz D, Schiesel M, Vassar M. Evaluating Reproducibility and Transparency in Emergency Medicine Publications. West J Emerg Med 2021; 22:963-971. [PMID: 35353995 PMCID: PMC8328179 DOI: 10.5811/westjem.2021.3.50078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 03/15/2021] [Accepted: 03/15/2021] [Indexed: 11/24/2022] Open
Abstract
INTRODUCTION We aimed to assess the reproducibility of empirical research by determining the availability of components required for replication of a study, including materials, raw data, analysis scripts, protocols, and preregistration. METHODS We used the National Library of Medicine catalog to identify MEDLINE-indexed emergency medicine (EM) journals. Thirty journals met the inclusion criteria. From January 1, 2014-December 31, 2018, 300 publications were randomly sampled using a PubMed search. Additionally, we included four high-impact general medicine journals, which added 106 publications. Two investigators were blinded for independent extraction. Extracted data included statements regarding the availability of materials, data, analysis scripts, protocols, and registration. RESULTS After the search, we found 25,473 articles, from which we randomly selected 300. Of the 300, only 287 articles met the inclusion criteria. Additionally, we added 106 publications from high-impact journals of which 77 met the inclusion criteria. Together, 364 publications were included, of which 212 articles contained empirical data to analyze. Of the eligible empirical articles, 2.49%, (95% confidence interval [CI], 0.33% to 4.64%] provided a material statement, 9.91% (95% CI, 5.88% to 13.93%) provided a data statement, 0 provided access to analysis scripts, 25.94% (95% CI, 20.04% to 31.84%) linked the protocol, and 39.15% (95% CI, 32.58% to 45.72%) were preregistered. CONCLUSION Studies in EM lack indicators required for reproducibility. The majority of studies fail to report factors needed to reproduce research to ensure credibility. Thus, an intervention is required and can be achieved through the collaboration of researchers, peer reviewers, funding agencies, and journals.
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Affiliation(s)
- Bradley S Johnson
- Oklahoma State University, Center for Health Sciences, Tulsa, Oklahoma
| | - Shelby Rauh
- Oklahoma State University, Center for Health Sciences, Tulsa, Oklahoma
| | - Daniel Tritz
- Oklahoma State University, Center for Health Sciences, Tulsa, Oklahoma
| | - Michael Schiesel
- Oklahoma State University Medical Center, Department of Emergency Medicine, Tulsa, Oklahoma
| | - Matt Vassar
- Oklahoma State University, Center for Health Sciences, Tulsa, Oklahoma
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Samota EK, Davey RP. Knowledge and Attitudes Among Life Scientists Toward Reproducibility Within Journal Articles: A Research Survey. Front Res Metr Anal 2021; 6:678554. [PMID: 34268467 PMCID: PMC8276979 DOI: 10.3389/frma.2021.678554] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 05/18/2021] [Indexed: 12/22/2022] Open
Abstract
We constructed a survey to understand how authors and scientists view the issues around reproducibility, focusing on interactive elements such as interactive figures embedded within online publications, as a solution for enabling the reproducibility of experiments. We report the views of 251 researchers, comprising authors who have published in eLIFE Sciences, and those who work at the Norwich Biosciences Institutes (NBI). The survey also outlines to what extent researchers are occupied with reproducing experiments themselves. Currently, there is an increasing range of tools that attempt to address the production of reproducible research by making code, data, and analyses available to the community for reuse. We wanted to collect information about attitudes around the consumer end of the spectrum, where life scientists interact with research outputs to interpret scientific results. Static plots and figures within articles are a central part of this interpretation, and therefore we asked respondents to consider various features for an interactive figure within a research article that would allow them to better understand and reproduce a published analysis. The majority (91%) of respondents reported that when authors describe their research methodology (methods and analyses) in detail, published research can become more reproducible. The respondents believe that having interactive figures in published papers is a beneficial element to themselves, the papers they read as well as to their readers. Whilst interactive figures are one potential solution for consuming the results of research more effectively to enable reproducibility, we also review the equally pressing technical and cultural demands on researchers that need to be addressed to achieve greater success in reproducibility in the life sciences.
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Affiliation(s)
- Evanthia Kaimaklioti Samota
- Earlham Institute, Norwich, United Kingdom
- School of Biological Sciences, University of East Anglia, Norwich, United Kingdom
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A meta-review of transparency and reproducibility-related reporting practices in published meta-analyses on clinical psychological interventions (2000-2020). Behav Res Methods 2021; 54:334-349. [PMID: 34173943 PMCID: PMC8863703 DOI: 10.3758/s13428-021-01644-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/02/2021] [Indexed: 12/12/2022]
Abstract
Meta-analysis is a powerful and important tool to synthesize the literature about a research topic. Like other kinds of research, meta-analyses must be reproducible to be compliant with the principles of the scientific method. Furthermore, reproducible meta-analyses can be easily updated with new data and reanalysed applying new and more refined analysis techniques. We attempted to empirically assess the prevalence of transparency and reproducibility-related reporting practices in published meta-analyses from clinical psychology by examining a random sample of 100 meta-analyses. Our purpose was to identify the key points that could be improved, with the aim of providing some recommendations for carrying out reproducible meta-analyses. We conducted a meta-review of meta-analyses of psychological interventions published between 2000 and 2020. We searched PubMed, PsycInfo and Web of Science databases. A structured coding form to assess transparency indicators was created based on previous studies and existing meta-analysis guidelines. We found major issues concerning: completely reproducible search procedures report, specification of the exact method to compute effect sizes, choice of weighting factors and estimators, lack of availability of the raw statistics used to compute the effect size and of interoperability of available data, and practically total absence of analysis script code sharing. Based on our findings, we conclude with recommendations intended to improve the transparency, openness, and reproducibility-related reporting practices of meta-analyses in clinical psychology and related areas.
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Clementi NC, Barba LA. Reproducible validation and replication studies in nanoscale physics. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200068. [PMID: 33775146 DOI: 10.1098/rsta.2020.0068] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/23/2020] [Indexed: 06/12/2023]
Abstract
Credibility building activities in computational research include verification and validation, reproducibility and replication, and uncertainty quantification. Though orthogonal to each other, they are related. This paper presents validation and replication studies in electromagnetic excitations on nanoscale structures, where the quantity of interest is the wavelength at which resonance peaks occur. The study uses the open-source software PyGBe: a boundary element solver with treecode acceleration and GPU capability. We replicate a result by Rockstuhl et al. (2005, doi:10/dsxw9d) with a two-dimensional boundary element method on silicon carbide (SiC) particles, despite differences in our method. The second replication case from Ellis et al. (2016, doi:10/f83zcb) looks at aspect ratio effects on high-order modes of localized surface phonon-polariton nanostructures. The results partially replicate: the wavenumber position of some modes match, but for other modes they differ. With virtually no information about the original simulations, explaining the discrepancies is not possible. A comparison with experiments that measured polarized reflectance of SiC nano pillars provides a validation case. The wavenumber of the dominant mode and two more do match, but differences remain in other minor modes. Results in this paper were produced with strict reproducibility practices, and we share reproducibility packages for all, including input files, execution scripts, secondary data, post-processing code and plotting scripts, and the figures (deposited in Zenodo). In view of the many challenges faced, we propose that reproducible practices make replication and validation more feasible. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.
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Affiliation(s)
- N C Clementi
- Department of Mechanical and Aerospace Engineering, The George Washington University, Washington DC, USA
| | - L A Barba
- Department of Mechanical and Aerospace Engineering, The George Washington University, Washington DC, USA
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Macleod M, Collings AM, Graf C, Kiermer V, Mellor D, Swaminathan S, Sweet D, Vinson V. The MDAR (Materials Design Analysis Reporting) Framework for transparent reporting in the life sciences. Proc Natl Acad Sci U S A 2021; 118:e2103238118. [PMID: 33893240 PMCID: PMC8092464 DOI: 10.1073/pnas.2103238118] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Affiliation(s)
- Malcolm Macleod
- Edinburgh CAMARADES group, Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh EH8 9YL, United Kingdom;
| | | | - Chris Graf
- John Wiley & Sons, Oxford OX4 2DQ, United Kingdom
| | | | - David Mellor
- Center for Open Science, Charlottesville, VA 22903;
| | | | | | - Valda Vinson
- Science, American Association for the Advancement of Science, Washington, DC 20005
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