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Jiang L, Lan M, Menke JD, Vorland CJ, Kilicoglu H. Text classification models for assessing the completeness of randomized controlled trial publications based on CONSORT reporting guidelines. Sci Rep 2024; 14:21721. [PMID: 39289403 PMCID: PMC11408668 DOI: 10.1038/s41598-024-72130-7] [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: 04/03/2024] [Accepted: 09/04/2024] [Indexed: 09/19/2024] Open
Abstract
Complete and transparent reporting of randomized controlled trial publications (RCTs) is essential for assessing their credibility. We aimed to develop text classification models for determining whether RCT publications report CONSORT checklist items. Using a corpus annotated with 37 fine-grained CONSORT items, we trained sentence classification models (PubMedBERT fine-tuning, BioGPT fine-tuning, and in-context learning with GPT-4) and compared their performance. We assessed the impact of data augmentation methods (Easy Data Augmentation (EDA), UMLS-EDA, text generation and rephrasing with GPT-4) on model performance. We also fine-tuned section-specific PubMedBERT models (e.g., Methods) to evaluate whether they 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). Fine-tuned PubMedBERT model that uses the sentence along with the surrounding sentences and section headers yielded the best overall performance (sentence level: 0.71 micro-F1, 0.67 macro-F1; article-level: 0.90 micro-F1, 0.84 macro-F1). Data augmentation had limited positive effect. BioGPT fine-tuning and GPT-4 in-context learning exhibited suboptimal results. Methods-specific model improved recognition of methodology items, other section-specific models did not have significant impact. 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.
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Affiliation(s)
- Lan Jiang
- School of Information Sciences, University of Illinois Urbana-Champaign, 501 E Daniel Street, Champaign, IL, 61820, USA
| | - Mengfei Lan
- School of Information Sciences, University of Illinois Urbana-Champaign, 501 E Daniel Street, Champaign, IL, 61820, USA
| | - Joe D Menke
- School of Information Sciences, University of Illinois Urbana-Champaign, 501 E Daniel Street, Champaign, IL, 61820, USA
| | - Colby J Vorland
- School of Public Health, Indiana University, Bloomington, IN, USA
| | - Halil Kilicoglu
- School of Information Sciences, University of Illinois Urbana-Champaign, 501 E Daniel Street, Champaign, IL, 61820, USA.
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Lan M, Cheng M, Hoang L, Ter Riet G, Kilicoglu H. Automatic categorization of self-acknowledged limitations in randomized controlled trial publications. J Biomed Inform 2024; 152:104628. [PMID: 38548008 DOI: 10.1016/j.jbi.2024.104628] [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: 10/02/2023] [Revised: 03/09/2024] [Accepted: 03/24/2024] [Indexed: 04/05/2024]
Abstract
OBJECTIVE Acknowledging study limitations in a scientific publication is a crucial element in scientific transparency and progress. However, limitation reporting is often inadequate. Natural language processing (NLP) methods could support automated reporting checks, improving research transparency. In this study, our objective was to develop a dataset and NLP methods to detect and categorize self-acknowledged limitations (e.g., sample size, blinding) reported in randomized controlled trial (RCT) publications. METHODS We created a data model of limitation types in RCT studies and annotated a corpus of 200 full-text RCT publications using this data model. We fine-tuned BERT-based sentence classification models to recognize the limitation sentences and their types. To address the small size of the annotated corpus, we experimented with data augmentation approaches, including Easy Data Augmentation (EDA) and Prompt-Based Data Augmentation (PromDA). We applied the best-performing model to a set of about 12K RCT publications to characterize self-acknowledged limitations at larger scale. RESULTS Our data model consists of 15 categories and 24 sub-categories (e.g., Population and its sub-category DiagnosticCriteria). We annotated 1090 instances of limitation types in 952 sentences (4.8 limitation sentences and 5.5 limitation types per article). A fine-tuned PubMedBERT model for limitation sentence classification improved upon our earlier model by about 1.5 absolute percentage points in F1 score (0.821 vs. 0.8) with statistical significance (p<.001). Our best-performing limitation type classification model, PubMedBERT fine-tuning with PromDA (Output View), achieved an F1 score of 0.7, improving upon the vanilla PubMedBERT model by 2.7 percentage points, with statistical significance (p<.001). CONCLUSION The model could support automated screening tools which can be used by journals to draw the authors' attention to reporting issues. Automatic extraction of limitations from RCT publications could benefit peer review and evidence synthesis, and support advanced methods to search and aggregate the evidence from the clinical trial literature.
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Affiliation(s)
- Mengfei Lan
- School of Information Sciences, University of Illinois Urbana-Champaign, 501 Daniel Street, Champaign, 61820, IL, USA
| | - Mandy Cheng
- Department of Biological Sciences, Binghamton University, 4400 Vestal Parkway East, New York City, 13902, NY, USA
| | - Linh Hoang
- School of Information Sciences, University of Illinois Urbana-Champaign, 501 Daniel Street, Champaign, 61820, IL, USA
| | - Gerben Ter Riet
- Faculty of Health, Amsterdam University of Applied Sciences, Tafelbergweg 51, Amsterdam, 1105 BD, The Netherlands
| | - Halil Kilicoglu
- School of Information Sciences, University of Illinois Urbana-Champaign, 501 Daniel Street, Champaign, 61820, IL, USA.
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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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Ott DE. Limitations in Medical Research: Recognition, Influence, and Warning. JSLS 2024; 28:e2023.00049. [PMID: 38405216 PMCID: PMC10882193 DOI: 10.4293/jsls.2023.00049] [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: 02/27/2024] Open
Abstract
Background As the number of limitations increases in a medical research article, their consequences multiply and the validity of findings decreases. How often do limitations occur in a medical article? What are the implications of limitation interaction? How often are the conclusions hedged in their explanation? Objective To identify the number, type, and frequency of limitations and words used to describe conclusion(s) in medical research articles. Methods Search, analysis, and evaluation of open access research articles from 2021 and 2022 from the Journal of the Society of Laparoscopic and Robotic Surgery and 2022 Surgical Endoscopy for type(s) of limitation(s) admitted to by author(s) and the number of times they occurred. Limitations not admitted to were found, obvious, and not claimed. An automated text analysis was performed for hedging words in conclusion statements. A limitation index score is proposed to gauge the validity of statements and conclusions as the number of limitations increases. Results A total of 298 articles were reviewed and analyzed, finding 1,764 limitations. Four articles had no limitations. The average was between 3.7% and 6.9% per article. Hedging, weasel words and words of estimative probability description was found in 95.6% of the conclusions. Conclusions Limitations and their number matter. The greater the number of limitations and ramifications of their effects, the more outcomes and conclusions are affected. Wording ambiguity using hedging or weasel words shows that limitations affect the uncertainty of claims. The limitation index scoring method shows the diminished validity of finding(s) and conclusion(s).
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Automatic recognition and classification of future work sentences from academic articles in a specific domain. J Informetr 2023. [DOI: 10.1016/j.joi.2022.101373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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Schulz R, Barnett A, Bernard R, Brown NJL, Byrne JA, Eckmann P, Gazda MA, Kilicoglu H, Prager EM, Salholz-Hillel M, Ter Riet G, Vines T, Vorland CJ, Zhuang H, Bandrowski A, Weissgerber TL. Is the future of peer review automated? BMC Res Notes 2022; 15:203. [PMID: 35690782 PMCID: PMC9188010 DOI: 10.1186/s13104-022-06080-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 05/18/2022] [Indexed: 12/19/2022] Open
Abstract
The rising rate of preprints and publications, combined with persistent inadequate reporting practices and problems with study design and execution, have strained the traditional peer review system. Automated screening tools could potentially enhance peer review by helping authors, journal editors, and reviewers to identify beneficial practices and common problems in preprints or submitted manuscripts. Tools can screen many papers quickly, and may be particularly helpful in assessing compliance with journal policies and with straightforward items in reporting guidelines. However, existing tools cannot understand or interpret the paper in the context of the scientific literature. Tools cannot yet determine whether the methods used are suitable to answer the research question, or whether the data support the authors' conclusions. Editors and peer reviewers are essential for assessing journal fit and the overall quality of a paper, including the experimental design, the soundness of the study's conclusions, potential impact and innovation. Automated screening tools cannot replace peer review, but may aid authors, reviewers, and editors in improving scientific papers. Strategies for responsible use of automated tools in peer review may include setting performance criteria for tools, transparently reporting tool performance and use, and training users to interpret reports.
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Affiliation(s)
- Robert Schulz
- BIH QUEST Center for Responsible Research, Berlin Institute of Health at Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Adrian Barnett
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health & Social Work, Queensland University of Technology, Brisbane, QLD, Australia
| | - René Bernard
- NeuroCure Cluster of Excellence, Charité Universitätsmedizin Berlin, Berlin, Germany
| | | | - Jennifer A Byrne
- Faculty of Medicine and Health, New South Wales Health Pathology, The University of Sydney, New South Wales, Australia
| | - Peter Eckmann
- Department of Neuroscience, University of California, San Diego, La Jolla, CA, USA
| | - Małgorzata A Gazda
- UMR 3525, Institut Pasteur, Université de Paris, CNRS, INSERM UA12, Comparative Functional Genomics group, Paris, France
| | - Halil Kilicoglu
- School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, IL, USA
| | - Eric M Prager
- Translational Research and Development, Cohen Veterans Bioscience, New York, NY, USA
| | - Maia Salholz-Hillel
- BIH QUEST Center for Responsible Research, Berlin Institute of Health at Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Gerben Ter Riet
- Faculty of Health, Center of Expertise Urban Vitality, Amsterdam University of Applied Science, Amsterdam, The Netherlands
| | - Timothy Vines
- DataSeer Research Data Services Ltd, Vancouver, BC, Canada
| | - Colby J Vorland
- Indiana University School of Public Health-Bloomington, Bloomington, IN, USA
| | - Han Zhuang
- School of Information Studies, Syracuse University, Syracuse, NY, USA
| | - Anita Bandrowski
- Department of Neuroscience, University of California, San Diego, La Jolla, CA, USA
| | - Tracey L Weissgerber
- BIH QUEST Center for Responsible Research, Berlin Institute of Health at Charité Universitätsmedizin Berlin, Berlin, Germany.
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Kilicoglu H, Rosemblat G, Hoang L, Wadhwa S, Peng Z, Malički M, Schneider J, Ter Riet G. Toward assessing clinical trial publications for reporting transparency. J Biomed Inform 2021; 116:103717. [PMID: 33647518 PMCID: PMC8112250 DOI: 10.1016/j.jbi.2021.103717] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Revised: 02/14/2021] [Accepted: 02/15/2021] [Indexed: 10/22/2022]
Abstract
OBJECTIVE To annotate a corpus of randomized controlled trial (RCT) publications with the checklist items of CONSORT reporting guidelines and using the corpus to develop text mining methods for RCT appraisal. METHODS We annotated a corpus of 50 RCT articles at the sentence level using 37 fine-grained CONSORT checklist items. A subset (31 articles) was double-annotated and adjudicated, while 19 were annotated by a single annotator and reconciled by another. We calculated inter-annotator agreement at the article and section level using MASI (Measuring Agreement on Set-Valued Items) and at the CONSORT item level using Krippendorff's α. We experimented with two rule-based methods (phrase-based and section header-based) and two supervised learning approaches (support vector machine and BioBERT-based neural network classifiers), for recognizing 17 methodology-related items in the RCT Methods sections. RESULTS We created CONSORT-TM consisting of 10,709 sentences, 4,845 (45%) of which were annotated with 5,246 labels. A median of 28 CONSORT items (out of possible 37) were annotated per article. Agreement was moderate at the article and section levels (average MASI: 0.60 and 0.64, respectively). Agreement varied considerably among individual checklist items (Krippendorff's α= 0.06-0.96). The model based on BioBERT performed best overall for recognizing methodology-related items (micro-precision: 0.82, micro-recall: 0.63, micro-F1: 0.71). Combining models using majority vote and label aggregation further improved precision and recall, respectively. CONCLUSION Our annotated corpus, CONSORT-TM, contains more fine-grained information than earlier RCT corpora. Low frequency of some CONSORT items made it difficult to train effective text mining models to recognize them. For the items commonly reported, CONSORT-TM can serve as a testbed for text mining methods that assess RCT transparency, rigor, and reliability, and support methods for peer review and authoring assistance. Minor modifications to the annotation scheme and a larger corpus could facilitate improved text mining models. CONSORT-TM is publicly available at https://github.com/kilicogluh/CONSORT-TM.
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Affiliation(s)
- Halil Kilicoglu
- School of Information Sciences, University of Illinois at Urbana-Champaign, Champaign, IL, USA; U.S. National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
| | - Graciela Rosemblat
- U.S. National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Linh Hoang
- School of Information Sciences, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Sahil Wadhwa
- Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Zeshan Peng
- U.S. National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Mario Malički
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, USA
| | - Jodi Schneider
- School of Information Sciences, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Gerben Ter Riet
- Urban Vitality Center of Expertise, Faculty of Health, Amsterdam University of Applied Sciences, Amsterdam, the Netherlands; Department of Cardiology Heart Center, Amsterdam UMC, University of Amsterdam, the Netherlands
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Weissgerber T, Riedel N, Kilicoglu H, Labbé C, Eckmann P, Ter Riet G, Byrne J, Cabanac G, Capes-Davis A, Favier B, Saladi S, Grabitz P, Bannach-Brown A, Schulz R, McCann S, Bernard R, Bandrowski A. Automated screening of COVID-19 preprints: can we help authors to improve transparency and reproducibility? Nat Med 2021; 27:6-7. [PMID: 33432174 PMCID: PMC8177099 DOI: 10.1038/s41591-020-01203-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Tracey Weissgerber
- Quality | Ethics | Open Science | Translation (QUEST), Berlin Institute of Health, Berlin, Germany.
- Charité-Universitätsmedizin Berlin, Berlin, Germany.
| | - Nico Riedel
- Quality | Ethics | Open Science | Translation (QUEST), Berlin Institute of Health, Berlin, Germany
| | - Halil Kilicoglu
- School of Information Sciences, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Cyril Labbé
- University Grenoble Alpes, CNRS, Grenoble INP, LIG, Grenoble, France
| | - Peter Eckmann
- Department of Neuroscience, University of California, San Diego, La Jolla, CA, USA
- SciCrunch Inc., San Diego, CA, USA
| | - Gerben Ter Riet
- Department of Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
- Urban Vitality Center of Expertise, Amsterdam University of Applied Sciences, Amsterdam, the Netherlands
| | - Jennifer Byrne
- New South Wales Health Statewide Biobank, New South Wales Health Pathology, Sydney, New South Wales, Australia
- Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | | | - Amanda Capes-Davis
- CellBank Australia, Children's Medical Research Institute and The University of Sydney, Westmead, NSW, Australia
| | | | - Shyam Saladi
- California Institute of Technology, Pasadena, CA, USA
| | - Peter Grabitz
- Quality | Ethics | Open Science | Translation (QUEST), Berlin Institute of Health, Berlin, Germany
- Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Alexandra Bannach-Brown
- Quality | Ethics | Open Science | Translation (QUEST), Berlin Institute of Health, Berlin, Germany
| | - Robert Schulz
- Quality | Ethics | Open Science | Translation (QUEST), Berlin Institute of Health, Berlin, Germany
- Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Sarah McCann
- Quality | Ethics | Open Science | Translation (QUEST), Berlin Institute of Health, Berlin, Germany
- Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Rene Bernard
- NeuroCure Cluster of Excellence, Charité-Universitätsmedizin Berlin, corporate member of the Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Anita Bandrowski
- Department of Neuroscience, University of California, San Diego, La Jolla, CA, USA
- SciCrunch Inc., San Diego, CA, USA
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Alvarez G, Núñez-Cortés R, Solà I, Sitjà-Rabert M, Fort-Vanmeerhaeghe A, Fernández C, Bonfill X, Urrútia G. Sample size, study length, and inadequate controls were the most common self-acknowledged limitations in manual therapy trials: A methodological review. J Clin Epidemiol 2020; 130:96-106. [PMID: 33144246 DOI: 10.1016/j.jclinepi.2020.10.018] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 10/23/2020] [Accepted: 10/28/2020] [Indexed: 02/06/2023]
Abstract
OBJECTIVES The aim of this study was to quantify and analyze the presence and type of self-acknowledged limitations (SALs) in a sample of manual therapy (MT) randomized controlled trials. STUDY DESIGN AND SETTING We randomly selected 120 MT trials. We extracted data related to SALs from the original reports and classified them into 12 categories. After data extraction, specific limitations within each category were identified. A descriptive analysis was performed using frequencies and percentages for qualitative variables. RESULTS The number of SALs per trial article ranged from 0 to 8, and more than two-thirds of trials acknowledged at least two different limitations. Despite its small proportion, 9% of trials did not report SALs. The most common limitation declared, in almost half of our sample, related to sample size (47.5%) followed by limitations related to study length and follow-up (33.3%) and inadequate controls (32.5%). CONCLUSION Our results indicate that at least two different limitations are consistently acknowledged in MT trial reports, the most common being those related to sample size, study length, follow-up, and inadequate controls. Analysis of the reasons behind the SALs gives some insights about the main difficulties in conducting research in this field and may help develop strategies to improve future research.
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Affiliation(s)
- Gerard Alvarez
- Iberoamerican Cochrane Centre - Sant Pau Biomedical Research Institute, IIB Sant Pau, Barcelona, Spain; Foundation Centre for Osteopathic Medicine Collaboration. Spain National Centre, Barcelona, Spain.
| | - Rodrigo Núñez-Cortés
- Department of Physical Therapy, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Ivan Solà
- Iberoamerican Cochrane Centre - Sant Pau Biomedical Research Institute, IIB Sant Pau, Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Mercè Sitjà-Rabert
- Blanquerna School of Health Science (FCS), Ramon Llull University, Barcelona, Spain; Global Research on Wellbeing (GRoW) Research Group, Ramon Llull University, Barcelona, Spain
| | - Azahara Fort-Vanmeerhaeghe
- Blanquerna School of Health Science (FCS), Ramon Llull University, Barcelona, Spain; Blanquerna Faculty of Psychology, Education Sciences and Sport (FPCEE), Ramon Llull University, Barcelona, Spain
| | - Carles Fernández
- Blanquerna School of Health Science (FCS), Ramon Llull University, Barcelona, Spain; Global Research on Wellbeing (GRoW) Research Group, Ramon Llull University, Barcelona, Spain
| | - Xavier Bonfill
- Iberoamerican Cochrane Centre - Sant Pau Biomedical Research Institute, IIB Sant Pau, Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Gerard Urrútia
- Iberoamerican Cochrane Centre - Sant Pau Biomedical Research Institute, IIB Sant Pau, Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
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Keserlioglu K, Kilicoglu H, Ter Riet G. Impact of peer review on discussion of study limitations and strength of claims in randomized trial reports: a before and after study. Res Integr Peer Rev 2019; 4:19. [PMID: 31534784 PMCID: PMC6745784 DOI: 10.1186/s41073-019-0078-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2019] [Accepted: 08/14/2019] [Indexed: 11/22/2022] Open
Abstract
Background In their research reports, scientists are expected to discuss limitations that their studies have. Previous research showed that often, such discussion is absent. Also, many journals emphasize the importance of avoiding overstatement of claims. We wanted to see to what extent editorial handling and peer review affects self-acknowledgment of limitations and hedging of claims. Methods Using software that automatically detects limitation-acknowledging sentences and calculates the level of hedging in sentences, we compared the submitted manuscripts and their ultimate publications of all randomized trials published in 2015 in 27 BioMed Central (BMC) journals and BMJ Open. We used mixed linear and logistic regression models, accounting for clustering of manuscript-publication pairs within journals, to quantify before-after changes in the mean numbers of limitation-acknowledging sentences, in the probability that a manuscript with zero self-acknowledged limitations ended up as a publication with at least one and in hedging scores. Results Four hundred forty-six manuscript-publication pairs were analyzed. The median number of manuscripts per journal was 10.5 (interquartile range 6–18). The average number of distinct limitation sentences increased by 1.39 (95% CI 1.09–1.76), from 2.48 in manuscripts to 3.87 in publications. Two hundred two manuscripts (45.3%) did not mention any limitations. Sixty-three (31%, 95% CI 25–38) of these mentioned at least one after peer review. Changes in mean hedging scores were negligible. Conclusions Our findings support the idea that editorial handling and peer review lead to more self-acknowledgment of study limitations, but not to changes in linguistic nuance.
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Affiliation(s)
- Kerem Keserlioglu
- Department of General Practice, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands
| | - Halil Kilicoglu
- 2Lister Hill National Center for Biomedical Communications, U.S. National Library of Medicine, Bethesda, MD USA
| | - Gerben Ter Riet
- 3Department of Cardiology, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105AZ Amsterdam, The Netherlands.,4ACHIEVE Centre for Applied Research, Amsterdam University of Applied Sciences, Tafelbergweg 51, 1105 BD Amsterdam, The Netherlands
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