Software tools for literature screening in systematic reviews in biomedical research

Treatment of myopic choroidal neovascularization: a network meta-analysis and review

PURPOSE

This is, to our knowledge, the first network meta-analysis aiming to compare all treatment modalities for myopic choroidal neovascularization (CNV).

METHODS

After the electronic databases were searched, two independent reviewers screened titles, abstracts, full-texts, and extracted information. Primary endpoints were change in visual outcome and central retinal thickness. We used a network meta-analysis to compare treatment outcomes in the early (≤ 6 months) and late (> 6 months) phase.

RESULTS

We included 34 studies (2,098 eyes) in our network meta-analysis. In the early phase, the use of anti-VEGF led to a gain of 14.1 letters (95% CI, 10.8-17.4) compared to untreated patients (p < 0.0001), 12.1 letters (95% CI, 8.3-15.8) to photodynamic therapy (PDT) (p < 0.0001), 7.5 (95% CI, 1.2-13.8) letters to intravitreal triamcinolone acetonide (TCA) (p = 0.019), and - 2.9 letters (95% CI, - 6.0-0.2) to the combination of anti-VEGF and PDT (p = 0.065). In the later phase, these results were largely maintained. There were no significant differences in visual outcomes between patients treated with 1 + PRN and 3 + PRN. However, the 1 + PRN group received 1.8 (SD 1.3), while the 3 + PRN group received 3.2 (SD 0.9) injections within 12 months (p < 0.0001).

CONCLUSION

This network meta-analysis confirms that anti-VEGF is the most effective treatment for myopic CNV using the 1 + PRN treatment strategy.

Potential roles of large language models in production of systematic reviews and meta-analyses

UNSTRUCTURED

Large language models (LLMs) like ChatGPT have become widely applied in the field of medical research. In the process of conducting systematic reviews, similar tools can be employed to expedite various steps, including defining clinical questions, literature search, document screening, information extraction, and language refinement, etc, thereby conserving resources and enhancing efficiency. However, when utilizing LLMs, attention should be given to transparent reporting, distinguishing between genuine and false content, and avoiding academic misconduct. This article reviews the potential roles of LLMs in the creation of systematic reviews and meta-analyses, elucidating their advantages, limitations, and future research directions, aiming to provide insights and guidance for authors involved in systematic reviews and meta-analyses.

Health aspects of vegan diets among children and adolescents: a systematic review and meta-analyses

Health effects of vegan diets among children and adolescents are a controversial public health topic. Thus, the aim of the present systematic review is to evaluate a broad range of health outcomes among vegan children and adolescents aged 0 to 18 years. 18 studies met the inclusion criteria (17 cross-sectional, 1 RCT). Meta-analyses showed lower protein, calcium, vitamin B2, saturated fatty acid, and cholesterol intakes, and lower ferritin, HDL and LDL levels as well as height in vegan compared to omnivorous children/adolescents. Higher intakes of carbohydrates, polyunsaturated fatty acids, fiber, folate, vitamins C and E, magnesium, iron, and potassium were observed in vegans. Blood levels of vitamin B12 were higher among vegan children due to supplement use. Single study results suggested further differences between vegan and non-vegan children, such as lower bone mineral content or urinary iodine among vegan children. Risk of Bias was rated as high or very high in 7 out of 18 studies. The certainty of evidence for the meta-analyses was low (n = 2) or very low (n = 46). Overall, the available evidence points to both risks and benefits associated with a vegan diet among children, although more and better designed studies are needed.

Tirzepatide-Induced Gastrointestinal Manifestations: A Systematic Review and Meta-Analysis

This systematic review and meta-analysis determine how frequently and how seriously gastrointestinal manifestations affect people with type 2 diabetes mellitus on tirzepatide. Tirzepatide is a recently developed drug that attempts to enhance type 2 diabetics' ability to regulate their blood sugar levels and promote weight reduction. Despite its potential benefits, clinical trials have revealed that the medication may lead to gastrointestinal side effects, including nausea, vomiting, decreased appetite, dyspepsia, constipation, and diarrhea. These side effects may negatively affect the drug's efficacy and patient tolerance. A comprehensive search of electronic databases such as PubMed, Web of Science, and Cochrane Library, was conducted to find pertinent studies reporting on the frequency and severity of gastrointestinal symptoms in type 2 diabetes patients receiving tirzepatide. This systematic review follows the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. Study selection, data extraction, and quality assessment were performed. Six randomized controlled trials with a total of 4,586 patients were included. Most patients received tirzepatide to regulate their blood sugar levels and promote weight reduction, and the comparators were placebo, glucagon-like peptide one receptor agonists drugs, and insulin degludec. The dose of tirzepatide was 5mg, 10mg, and 15mg weekly. The incidence rate of nausea in patients who receive tirzepatide was 20.43%, while the incidence rate in the comparators was 10.47%, and it was significantly higher in the tirzepatide arm than in the comparators (RR, 2.90; 95% CI, 1.89 to 4.44; P ≤ 0.00001). The incidence rate of vomiting in patients who receive tirzepatide was 9.05%, while the rate in the comparators was 4.86%, and it was significantly higher in the tirzepatide arm than in the comparators (RR, 2.69; 95% CI, 1.67 to 4.36; P ≤ 0.0001). The incidence rate of constipation in patients who receive tirzepatide was 2.54%, while the rate in the comparators was 0.856%, and it was significantly higher in the tirzepatide arm than in the comparators (RR, 3.08; 95% CI, 1.83 to 5.20; P ≤ 0.0001). The incidence rate of decreased appetite in patients who receive tirzepatide was 9.64%, while the rate in the comparators was 2.88%, and it was significantly higher in the tirzepatide arm than in the comparators (RR, 5.04; 95% CI, 3.01 to 8.45; P ≤ 0.00001). The incidence rate of diarrhea in patients who receive tirzepatide was 16.24%, while the rate in the comparators was 8.63%, and it was significantly higher in the tirzepatide arm than in the comparators (RR, 2.07; 95% CI, 1.60 to 2.68; P ≤ 0.00001). The incidence rate of dyspepsia in patients who receive tirzepatide was 7.13%, while the rate in the comparators was 3.31%, and it was significantly higher in the tirzepatide arm than in the comparators (RR, 2.52; 95% CI, 1.58 to 4.01; P ≤ 0.0001). Tirzepatide usage is linked to a significant prevalence of gastrointestinal symptoms, including nausea, constipation, decreased appetite, dyspepsia, diarrhea, and vomiting, in people with type 2 diabetes. These findings may influence clinical decision-making and patient counseling on the use of tirzepatide and have significant implications for the medication's tolerance and efficacy. To find ways to reduce these negative effects and improve therapy for type 2 diabetes patients, more research is required.

Glucocorticoids in relation to behavior, morphology, and physiology as proxy indicators for the assessment of animal welfare. A systematic mapping review

Translating theoretical concepts of animal welfare into quantitative assessment protocols is an ongoing challenge. Glucocorticoids (GCs) are frequently used as physiological measure in welfare assessment. The interpretation of levels of GCs and especially their relation to welfare, however, is not as straightforward, questioning the informative power of GCs. The aim of this systematic mapping review was therefore to provide an overview of the relevant literature to identify global patterns in studies using GCs as proxy for the assessment of welfare of vertebrate species. Following a systematic protocol and a-priory inclusion criteria, 509 studies with 517 experiments were selected for data extraction. The outcome of the experiments was categorized based on whether the intervention significantly affected levels of GCs, and whether these effects were accompanied by changes in behavior, morphology and physiology. Additional information, such as animal species, type of intervention, experimental set up and sample type used for GC determination was extracted, as well. Given the broad scope and large variation in included experiments, meta-analyses were not performed, but outcomes are presented to encourage further, in-depth analyses of the data set. The interventions did not consistently lead to changes in GCs with respect to the original authors hypothesis. Changes in GCs were not consistently paralleled by changes in additional assessment parameter on behavior, morphology and physiology. The minority of experiment quantified GCs in less invasive sample matrices compared to blood. Interventions showed a large variability, and species such as fish were underrepresented, especially in the assessment of behavior. The inconclusive effects on GCs and additional assessment parameter urges for further validation of techniques and welfare proxies. Several conceptual and technical challenges need to be met to create standardized and robust welfare assessment protocols and to determine the role of GCs herein.

Combining globally search for a regular expression and print matching lines with bibliographic monitoring of genomic database improves diagnosis

Introduction: Exome sequencing has a diagnostic yield ranging from 25% to 70% in rare diseases and regularly implicates genes in novel disorders. Retrospective data reanalysis has demonstrated strong efficacy in improving diagnosis, but poses organizational difficulties for clinical laboratories. Patients and methods: We applied a reanalysis strategy based on intensive prospective bibliographic monitoring along with direct application of the GREP command-line tool (to "globally search for a regular expression and print matching lines") in a large ES database. For 18 months, we submitted the same five keywords of interest [(intellectual disability, (neuro)developmental delay, and (neuro)developmental disorder)] to PubMed on a daily basis to identify recently published novel disease-gene associations or new phenotypes in genes already implicated in human pathology. We used the Linux GREP tool and an in-house script to collect all variants of these genes from our 5,459 exome database. Results: After GREP queries and variant filtration, we identified 128 genes of interest and collected 56 candidate variants from 53 individuals. We confirmed causal diagnosis for 19/128 genes (15%) in 21 individuals and identified variants of unknown significance for 19/128 genes (15%) in 23 individuals. Altogether, GREP queries for only 128 genes over a period of 18 months permitted a causal diagnosis to be established in 21/2875 undiagnosed affected probands (0.7%). Conclusion: The GREP query strategy is efficient and less tedious than complete periodic reanalysis. It is an interesting reanalysis strategy to improve diagnosis.

Grimace scale, burrowing, and nest building for the assessment of post-surgical pain in mice and rats-A systematic review

Several studies suggested an informative value of behavioral and grimace scale parameters for the detection of pain. However, the robustness and reliability of the parameters as well as the current extent of implementation are still largely unknown. In this study, we aimed to systematically analyze the current evidence-base of grimace scale, burrowing, and nest building for the assessment of post-surgical pain in mice and rats. The following platforms were searched for relevant articles: PubMed, Embase via Ovid, and Web of Science. Only full peer-reviewed studies that describe the grimace scale, burrowing, and/or nest building as pain parameters in the post-surgical phase in mice and/or rats were included. Information about the study design, animal characteristics, intervention characteristics, and outcome measures was extracted from identified publications. In total, 74 papers were included in this review. The majority of studies have been conducted in young adult C57BL/6J mice and Sprague Dawley and Wistar rats. While there is an apparent lack of information about young animals, some studies that analyzed the grimace scale in aged rats were identified. The majority of studies focused on laparotomy-associated pain. Only limited information is available about other types of surgical interventions. While an impact of surgery and an influence of analgesia were rather consistently reported in studies focusing on grimace scales, the number of studies that assessed respective effects was rather low for nest building and burrowing. Moreover, controversial findings were evident for the impact of analgesics on post-surgical nest building activity. Regarding analgesia, a monotherapeutic approach was identified in the vast majority of studies with non-steroidal anti-inflammatory (NSAID) drugs and opioids being most commonly used. In conclusion, most evidence exists for grimace scales, which were more frequently used to assess post-surgical pain in rodents than the other behavioral parameters. However, our findings also point to relevant knowledge gaps concerning the post-surgical application in different strains, age levels, and following different surgical procedures. Future efforts are also necessary to directly compare the sensitivity and robustness of different readout parameters applied for the assessment of nest building and burrowing activities.

Deploying elements of scoping review methods for Adverse Outcome Pathway development: A space travel case example

Purpose Health protection agencies require scientific information for evidence-based decision-making and guideline development. However, vetting and collating large quantities of published research to identify relevant high-quality studies is a challenge. One approach to address this issue is the use of Adverse Outcome Pathways (AOPs) that provide a framework to assemble toxicological knowledge into causally linked chains of key events across levels of biological organization to culminate in an adverse health outcome of significance. Traditionally, AOPs have been constructed using a narrative review approach where the collection of evidence that supports each pathway is based on prior knowledge of influential studies that can also be supplemented by individually selecting and reviewing relevant references. Objectives: We aimed to create a protocol for AOP weight of evidence gathering that harnesses elements of both scoping review methods and artificial intelligence tools to increase transparency while reducing bias and workload of human screeners. Methods: To develop this protocol, an existing space-health AOP in the workplan of the Organisation for Economic Co-operation and Development (OECD) AOP program was used as a case example. To balance the benefits of both scoping review tools and narrative approaches, a study protocol outlining a screening and search strategy was developed, and three reference collection workflows were tested to identify the most efficient method to inform weight of evidence. The workflows differed in their literature search strategies, and combinations of software tools used. Results: Across the three tested workflows, over 59 literature searches were completed, retrieving over 34000 references of which over 3300 were human reviewed. The most effective of the three methods used a search strategy with searches across each component of the AOP network, SWIFT Review as a pre-filtering software, and DistillerSR to create structured screening and data extraction forms. This methodology effectively retrieved relevant studies while balancing efficiency in data retrieval without compromising transparency, leading to a well-synthesized evidence base to support the AOP. Conclusions: The workflow is still exploratory in the context of AOP development, and we anticipate adaptations to the protocol with further experience. To further the systematicity, future iterations of the workflow could include structured quality assessment and risk of bias analysis. Overall, the workflow provides a transparent and unbiased approach to support AOP development, which in turn will support the need for rigorous methods to identify relevant scientific evidence while being practical to allow uptake by the broader community.

Academic Output in Global Surgery after the Lancet Commission on Global Surgery: A Scoping Review

Background

The Lancet Commission on Global Surgery (LCoGS) published its seminal report in 2015, carving a niche for global surgery academia. Six years after the LCoGS, a scoping review was conducted to see how the term 'global surgery' is characterized by the literature and how it relates to LCoGS and its domains.

Methods

PubMed was searched for publications between January 2015 and February 2021 that used the term ‘global surgery’ in the title, abstract, or key words or cited the LCoGS. Variables extracted included LCoGS domains, authorship metrics, geographic scope, and clinical specialty.

Results

The search captured 938 articles that qualified for data extraction. Nearly 80% of first and last authors had high-income country affiliations. Africa was the most frequently investigated region, though many countries within the region were under-represented. The World Journal of Surgery was the most frequent journal, publishing 13.9% of all articles. General surgery, pediatric surgery, and neurosurgery were the most represented specialties. Of the LCoGS domains, healthcare delivery and management were the most studied, while economics and financing were the least studied.

Conclusion

A lack of consensus on the definition of global surgery remains. Additional research is needed in economics and financing, while obstetrics and trauma are under-represented in literature using the term ‘global surgery’. Efforts in academic global surgery must give a voice to those carrying the global surgery agenda forward on the frontlines. Focusing on research capacity-building and encouraging contribution by local partners will lead to a stronger, more cohesive global surgery community.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00268-022-06640-8.

Towards a qAOP framework for predictive toxicology - Linking data to decisions

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Chemical toxicity assessment depends on the quantification of kinetics and dynamics.

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Quantitative AOPs (qAOPs) are toxicodynamic models based on Adverse Outcome Pathways.

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Existing e-resources could form the basis of an e-infrastructure for qAOP modelling.

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Best practices for qAOP development, assessment and application are needed.

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Three qAOP case studies are presented to illustrate a modelling workflow.

The adverse outcome pathway (AOP) is a conceptual construct that facilitates organisation and interpretation of mechanistic data representing multiple biological levels and deriving from a range of methodological approaches including in silico, in vitro and in vivo assays. AOPs are playing an increasingly important role in the chemical safety assessment paradigm and quantification of AOPs is an important step towards a more reliable prediction of chemically induced adverse effects. Modelling methodologies require the identification, extraction and use of reliable data and information to support the inclusion of quantitative considerations in AOP development. An extensive and growing range of digital resources are available to support the modelling of quantitative AOPs, providing a wide range of information, but also requiring guidance for their practical application. A framework for qAOP development is proposed based on feedback from a group of experts and three qAOP case studies. The proposed framework provides a harmonised approach for both regulators and scientists working in this area.

Toward Automated Data Extraction According to Tabular Data Structure: Cross-sectional Pilot Survey of the Comparative Clinical Literature

BACKGROUND

Systematic reviews depend on time-consuming extraction of data from the PDFs of underlying studies. To date, automation efforts have focused on extracting data from the text, and no approach has yet succeeded in fully automating ingestion of quantitative evidence. However, the majority of relevant data is generally presented in tables, and the tabular structure is more amenable to automated extraction than free text.

OBJECTIVE

The purpose of this study was to classify the structure and format of descriptive statistics reported in tables in the comparative medical literature.

METHODS

We sampled 100 published randomized controlled trials from 2019 based on a search in PubMed; these results were imported to the AutoLit platform. Studies were excluded if they were nonclinical, noncomparative, not in English, protocols, or not available in full text. In AutoLit, tables reporting baseline or outcome data in all studies were characterized based on reporting practices. Measurement context, meaning the structure in which the interventions of interest, patient arm breakdown, measurement time points, and data element descriptions were presented, was classified based on the number of contextual pieces and metadata reported. The statistic formats for reported metrics (specific instances of reporting of data elements) were then classified by location and broken down into reporting strategies for continuous, dichotomous, and categorical metrics.

RESULTS

We included 78 of 100 sampled studies, one of which (1.3%) did not report data elements in tables. The remaining 77 studies reported baseline and outcome data in 174 tables, and 96% (69/72) of these tables broke down reporting by patient arms. Fifteen structures were found for the reporting of measurement context, which were broadly grouped into: 1×1 contexts, where two pieces of context are reported in total (eg, arms in columns, data elements in rows); 2×1 contexts, where two pieces of context are given on row headers (eg, time points in columns, arms nested in data elements on rows); and 1×2 contexts, where two pieces of context are given on column headers. The 1×1 contexts were present in 57% of tables (99/174), compared to 20% (34/174) for 2×1 contexts and 15% (26/174) for 1×2 contexts; the remaining 8% (15/174) used unique/other stratification methods. Statistic formats were reported in the headers or descriptions of 84% (65/74) of studies.

CONCLUSIONS

In this cross-sectional pilot review, we found a high density of information in tables, but with major heterogeneity in presentation of measurement context. The highest-density studies reported both baseline and outcome measures in tables, with arm-level breakout, intervention labels, and arm sizes present, and reported both the statistic formats and units. The measurement context formats presented here, broadly classified into three classes that cover 92% (71/78) of studies, form a basis for understanding the frequency of different reporting styles, supporting automated detection of the data format for extraction of metrics.

Web-Based Software Tools for Systematic Literature Review in Medicine: A Review and Feature Analysis (Preprint)

Background

Systematic reviews (SRs) are central to evaluating therapies but have high costs in terms of both time and money. Many software tools exist to assist with SRs, but most tools do not support the full process, and transparency and replicability of SR depend on performing and presenting evidence according to established best practices.

Objective

This study aims to provide a basis for comparing and selecting between web-based software tools that support SR, by conducting a feature-by-feature comparison of SR tools.

Methods

We searched for SR tools by reviewing any such tool listed in the SR Toolbox, previous reviews of SR tools, and qualitative Google searching. We included all SR tools that were currently functional and required no coding, and excluded reference managers, desktop applications, and statistical software. The list of features to assess was populated by combining all features assessed in 4 previous reviews of SR tools; we also added 5 features (manual addition, screening automation, dual extraction, living review, and public outputs) that were independently noted as best practices or enhancements of transparency and replicability. Then, 2 reviewers assigned binary present or absent assessments to all SR tools with respect to all features, and a third reviewer adjudicated all disagreements.

Results

Of the 53 SR tools found, 55% (29/53) were excluded, leaving 45% (24/53) for assessment. In total, 30 features were assessed across 6 classes, and the interobserver agreement was 86.46%. DistillerSR (Evidence Partners; 26/30, 87%), Nested Knowledge (Nested Knowledge; 25/30, 83%), and EPPI-Reviewer Web (EPPI-Centre; 24/30, 80%) support the most features followed by Giotto Compliance (Giotto Compliance; 23/30, 77%), LitStream (ICF), and SRDB.PRO (VTS Software). Fewer than half of all the features assessed are supported by 7 tools: RobotAnalyst (National Centre for Text Mining), SRDR (Agency for Healthcare Research and Quality), SyRF (Systematic Review Facility), Data Abstraction Assistant (Center for Evidence Synthesis in Health), SR Accelerator (Institute for Evidence-Based Healthcare), RobotReviewer (RobotReviewer), and COVID-NMA (COVID-NMA). Notably, of the 24 tools, only 10 (42%) support direct search, only 7 (29%) offer dual extraction, and only 13 (54%) offer living/updatable reviews.

Conclusions

DistillerSR, Nested Knowledge, and EPPI-Reviewer Web each offer a high density of SR-focused web-based tools. By transparent comparison and discussion regarding SR tool functionality, the medical community can both choose among existing software offerings and note the areas of growth needed, most notably in the support of living reviews.

Sysrev: A FAIR Platform for Data Curation and Systematic Evidence Review

Well-curated datasets are essential to evidence based decision making and to the integration of artificial intelligence with human reasoning across disciplines. However, many sources of data remain siloed, unstructured, and/or unavailable for complementary and secondary research. Sysrev was developed to address these issues. First, Sysrev was built to aid in systematic evidence reviews (SER), where digital documents are evaluated according to a well defined process, and where Sysrev provides an easy to access, publicly available and free platform for collaborating in SER projects. Secondly, Sysrev addresses the issue of unstructured, siloed, and inaccessible data in the context of generalized data extraction, where human and machine learning algorithms are combined to extract insights and evidence for better decision making across disciplines. Sysrev uses FAIR - Findability, Accessibility, Interoperability, and Reuse of digital assets - as primary principles in design. Sysrev was developed primarily because of an observed need to reduce redundancy, reduce inefficient use of human time and increase the impact of evidence based decision making. This publication is an introduction to Sysrev as a novel technology, with an overview of the features, motivations and use cases of the tool. Methods: Sysrev. com is a FAIR motivated web platform for data curation and SER. Sysrev allows users to create data curation projects called "sysrevs" wherein users upload documents, define review tasks, recruit reviewers, perform review tasks, and automate review tasks. Conclusion: Sysrev is a web application designed to facilitate data curation and SERs. Thousands of publicly accessible Sysrev projects have been created, accommodating research in a wide variety of disciplines. Described use cases include data curation, managed reviews, and SERs.

Augmented Reality, Virtual Reality and Artificial Intelligence in Orthopedic Surgery: A Systematic Review

Background: The application of virtual and augmented reality technologies to orthopaedic surgery training and practice aims to increase the safety and accuracy of procedures and reducing complications and costs. The purpose of this systematic review is to summarise the present literature on this topic while providing a detailed analysis of current flaws and benefits. Methods: A comprehensive search on the PubMed, Cochrane, CINAHL, and Embase database was conducted from inception to February 2021. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines were used to improve the reporting of the review. The Cochrane Risk of Bias Tool and the Methodological Index for Non-Randomized Studies (MINORS) was used to assess the quality and potential bias of the included randomized and non-randomized control trials, respectively. Results: Virtual reality has been proven revolutionary for both resident training and preoperative planning. Thanks to augmented reality, orthopaedic surgeons could carry out procedures faster and more accurately, improving overall safety. Artificial intelligence (AI) is a promising technology with limitless potential, but, nowadays, its use in orthopaedic surgery is limited to preoperative diagnosis. Conclusions: Extended reality technologies have the potential to reform orthopaedic training and practice, providing an opportunity for unidirectional growth towards a patient-centred approach.

Reviewing the animal literature: how to describe and choose between different types of literature reviews

Before starting any (animal) research project, review of the existing literature is good practice. From both the scientific and the ethical perspective, high-quality literature reviews are essential. Literature reviews have many potential advantages besides synthesising the evidence for a research question. First, they can show if a proposed study has already been performed, preventing redundant research. Second, when planning new experiments, reviews can inform the experimental design, thereby increasing the reliability, relevance and efficiency of the study. Third, reviews may even answer research questions using already available data. Multiple definitions of the term literature review co-exist. In this paper, we describe the different steps in the review process, and the risks and benefits of using various methodologies in each step. We then suggest common terminology for different review types: narrative reviews, mapping reviews, scoping reviews, rapid reviews, systematic reviews and umbrella reviews. We recommend which review to select, depending on the research question and available resources. We believe that improved understanding of review methods and terminology will prevent ambiguity and increase appropriate interpretation of the conclusions of reviews.

Development and uptake of an online systematic review platform: the early years of the CAMARADES Systematic Review Facility (SyRF)

Preclinical research is a vital step in the drug discovery pipeline and more generally in helping to better understand human disease aetiology and its management. Systematic reviews (SRs) can be powerful in summarising and appraising this evidence concerning a specific research question, to highlight areas of improvements, areas for further research and areas where evidence may be sufficient to take forward to other research domains, for instance clinical trial. Guidance and tools for preclinical research synthesis remain limited despite their clear utility. We aimed to create an online end-to-end platform primarily for conducting SRs of preclinical studies, that was flexible enough to support a wide variety of experimental designs, was adaptable to different research questions, would allow users to adopt emerging automated tools and support them during their review process using best practice. In this article, we introduce the Systematic Review Facility (https://syrf.org.uk), which was launched in 2016 and designed to support primarily preclinical SRs from small independent projects to large, crowdsourced projects. We discuss the architecture of the app and its features, including the opportunity to collaborate easily, to efficiently manage projects, to screen and annotate studies for important features (metadata), to extract outcome data into a secure database, and tailor these steps to each project. We introduce how we are working to leverage the use of automation tools and allow the integration of these services to accelerate and automate steps in the systematic review workflow.

Development, testing and use of data extraction forms in systematic reviews: a review of methodological guidance

Background

Data extraction forms link systematic reviews with primary research and provide the foundation for appraising, analysing, summarising and interpreting a body of evidence. This makes their development, pilot testing and use a crucial part of the systematic reviews process. Several studies have shown that data extraction errors are frequent in systematic reviews, especially regarding outcome data.

Methods

We reviewed guidance on the development and pilot testing of data extraction forms and the data extraction process. We reviewed four types of sources: 1) methodological handbooks of systematic review organisations (SRO); 2) textbooks on conducting systematic reviews; 3) method documents from health technology assessment (HTA) agencies and 4) journal articles. HTA documents were retrieved in February 2019 and database searches conducted in December 2019. One author extracted the recommendations and a second author checked them for accuracy. Results are presented descriptively.

Results

Our analysis includes recommendations from 25 documents: 4 SRO handbooks, 11 textbooks, 5 HTA method documents and 5 journal articles. Across these sources the most common recommendations on form development are to use customized or adapted standardised extraction forms (14/25); provide detailed instructions on their use (10/25); ensure clear and consistent coding and response options (9/25); plan in advance which data are needed (9/25); obtain additional data if required (8/25); and link multiple reports of the same study (8/25). The most frequent recommendations on piloting extractions forms are that forms should be piloted on a sample of studies (18/25); and that data extractors should be trained in the use of the forms (7/25). The most frequent recommendations on data extraction are that extraction should be conducted by at least two people (17/25); that independent parallel extraction should be used (11/25); and that procedures to resolve disagreements between data extractors should be in place (14/25).

Conclusions

Overall, our results suggest a lack of comprehensiveness of recommendations. This may be particularly problematic for less experienced reviewers. Limitations of our method are the scoping nature of the review and that we did not analyse internal documents of health technology agencies.

The Effect of Caregiver-Mediated Mobility Interventions in Hospitalized Patients on Patient, Caregiver, and Health System Outcomes: A Systematic Review

OBJECTIVE

To synthesize the evidence examining caregiver-mediated mobility interventions in a hospital setting and whether they improve patient, caregiver, or health system outcomes.

DATA SOURCES

We searched MEDLINE, EMBASE, PsycINFO, CINAHL, and Scopus databases from inception to September 7, 2018.

STUDY SELECTION

Two reviewers independently selected original research in inpatient settings that reported on an intervention delivered by a caregiver (eg, family, friend, paid worker) and directed to the patient's mobility. Mobility interventions were categorized based on the level of caregiver engagement using a 3-category framework: inform (provision of education on patient's condition and management), activate (prompting caregivers to take action in patient care), and collaborate (encouraging interaction with providers or other caregivers).

DATA EXTRACTION

One reviewer extracted data, and another checked the data. Quality was assessed using the Cochrane Collaboration's risk of bias tool and Grading of Recommendations, Assessment, Development and Evaluation approach.

DATA SYNTHESIS

Forty studies met the inclusion criteria; most were randomized controlled trials (n=16/40, 40.0%) and investigated older adults (n=18/40, 45.0%) with stroke (n=20/40, 50.0%). Inform (n=2) and activate (n=4) interventions and combined inform-activate (n=5/6, 83.3%) and inform-activate-collaborate (n=6/10, 60.0%) interventions were reported to improve patient mobility. Inform-activate and inform-collaborate interventions were reported to improve caregiver outcomes (eg, burden) (n=13/19, 68.4%). Studies that engaged caregivers in all 3 strategies (inform-activate-collaborate) were reported to improve health system outcomes (eg, hospital readmission) (n=4/6, 66.7%). Most studies were of unclear (n=22/40, 55.0%) or low risk of bias (n=11/40, 27.5%) for most domains.

CONCLUSIONS

Engaging caregivers in mobility of hospitalized patients may improve patient mobility as well as caregiver and health system outcomes.

A Microsoft Excel Approach to Reduce Errors and Increase Efficiency in Systematic Searching

Developing a search strategy for a systematic review is a time-consuming process in which small errors around the formatting and compilation of terms can have large consequences. Microsoft Excel was identified as a potentially useful software to streamline the process and reduce manual errors. Ultimately a spreadsheet was created that largely automates the process of creating a single-line search string with correctly formatted terms, Boolean operators and parentheses.

What are Systematic Reviews?

Systematic reviews are a type of review that uses repeatable analytical methods to collect secondary data and analyse it. Systematic reviews are a type of evidence synthesis which formulate research questions that are broad or narrow in scope, and identify and synthesize data that directly relate to the systematic review question. While some people might associate ‘systematic review’ with 'meta-analysis', there are multiple kinds of review which can be defined as ‘systematic’ which do not involve a meta-analysis. Some systematic reviews critically appraise research studies, and synthesize findings qualitatively or quantitatively. Systematic reviews are often designed to provide an exhaustive summary of current evidence relevant to a research question. For example, systematic reviews of randomized controlled trials are an important way of informing evidence-based medicine, and a review of existing studies is often quicker and cheaper than embarking on a new study. While systematic reviews are often applied in the biomedical or healthcare context, they can be used in other areas where an assessment of a precisely defined subject would be helpful. Systematic reviews may examine clinical tests, public health interventions, environmental interventions, social interventions, adverse effects, qualitative evidence syntheses, methodological reviews, policy reviews, and economic evaluations. An understanding of systematic reviews and how to implement them in practice is highly recommended for professionals involved in the delivery of health care, public health and public policy.