Explore the vast range of titles available.

A randomized controlled trial (RCT) may be intended either to support real-world decisions on choice between alternative interventions or to help researchers understand mechanisms of action of an intervention. PRECIS-2 is widely used to help investigators match detailed design elements to their main intention for that RCT. PRECIS-2 is increasingly being used retrospectively for assessing RCTs within reviews. In this commentary, we counter arguments that RCTs with a placebo control group, masking/blinding of participants or providers, or conducted in a single center should be retrospectively assessed as completely explanatory, overriding a detailed PRECIS-2 assessment. We also counter arguments that a trial cannot be assessed using only the main peer-reviewed trial report. This is a commentary on the use of PRECIS-2 for systematic reviews. Although placebos are seldom openly prescribed in real-world care, knowing that an intervention achieves its impact via the placebo effect might change some clinical and policy decisions, which means that this feature does not always preclude decision-making use and so should not override a full PRECIS-2 assessment. A domain describing the comparator should be added to PRECIS-2. Conduct of an RCT in only a single centre should also not override PRECIS-2 as the decision support value of a single-centre RCT could be high for decision makers in that centre and others like it. Many journals require that submitted RCT reports meet CONSORT reporting guidelines, which standardizes the available information for all RCTs in systematic reviews; whereas information from registration and protocol documents is unstandardized and undermines comparison between RCTs and across reviews. Published RCT reports are thus more suitable for retrospective PRECIS-2 assessments, but PRECIS-2 domains with missing information should be scored as blank. Wider use of the CONSORT extension specific to pragmatic trials may reduce domains with missing data. PRECIS-2 can be used for retrospective assessments of trials in systematic reviews. The PRECIS-2 instrument should be expanded by including a domain describing the control group(s). Published RCT reports are suitable for retrospective PRECIS-2 assessments.

The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.

The methods and results of systematic reviews should be reported in sufficient detail to allow users to assess the trustworthiness and applicability of the review findings. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement was developed to facilitate transparent and complete reporting of systematic reviews and has been updated (to PRISMA 2020) to reflect recent advances in systematic review methodology and terminology. Here, we present the explanation and elaboration paper for PRISMA 2020, where we explain why reporting of each item is recommended, present bullet points that detail the reporting recommendations, and present examples from published reviews. We hope that changes to the content and structure of PRISMA 2020 will facilitate uptake of the guideline and lead to more transparent, complete, and accurate reporting of systematic reviews.

The funders had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication. Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: EL is head of research for the BMJ; MJP is an editorial board member for PLOS Medicine; ACT is an associate editor and MJP, TL, EMW, and DM are editorial board members for the Journal of Clinical Epidemiology; DM and LAS were editors in chief, LS, JMT, and ACT are associate editors, and JG is an editorial board member for Systematic Reviews. [...]technological advances have enabled the use of natural language processing and machine learning to identify relevant evidence,[22–24] methods have been proposed to synthesise and present findings when meta-analysis is not possible or appropriate,[25–27] and new methods have been developed to assess the risk of bias in results of included studies. Summary points * To ensure a systematic review is valuable to users, authors should prepare a transparent, complete, and accurate account of why the review was done, what they did, and what they found * The PRISMA 2020 statement provides updated reporting guidance for systematic reviews that reflects advances in methods to identify, select, appraise, and synthesise studies * The PRISMA 2020 statement consists of a 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and revised flow diagrams for original and updated reviews * We anticipate that the PRISMA 2020 statement will benefit authors, editors, and peer reviewers of systematic reviews, and different users of reviews, including guideline developers, policy makers, healthcare providers, patients, and other stakeholders Development of PRISMA 2020 A complete description of the methods used to develop PRISMA 2020 is available elsewhere.

This study developed, calibrated, and evaluated a machine learning classifier designed to reduce study identification workload in Cochrane for producing systematic reviews. A machine learning classifier for retrieving randomized controlled trials (RCTs) was developed (the “Cochrane RCT Classifier”), with the algorithm trained using a data set of title–abstract records from Embase, manually labeled by the Cochrane Crowd. The classifier was then calibrated using a further data set of similar records manually labeled by the Clinical Hedges team, aiming for 99% recall. Finally, the recall of the calibrated classifier was evaluated using records of RCTs included in Cochrane Reviews that had abstracts of sufficient length to allow machine classification. The Cochrane RCT Classifier was trained using 280,620 records (20,454 of which reported RCTs). A classification threshold was set using 49,025 calibration records (1,587 of which reported RCTs), and our bootstrap validation found the classifier had recall of 0.99 (95% confidence interval 0.98–0.99) and precision of 0.08 (95% confidence interval 0.06–0.12) in this data set. The final, calibrated RCT classifier correctly retrieved 43,783 (99.5%) of 44,007 RCTs included in Cochrane Reviews but missed 224 (0.5%). Older records were more likely to be missed than those more recently published. The Cochrane RCT Classifier can reduce manual study identification workload for Cochrane Reviews, with a very low and acceptable risk of missing eligible RCTs. This classifier now forms part of the Evidence Pipeline, an integrated workflow deployed within Cochrane to help improve the efficiency of the study identification processes that support systematic review production. •Systematic review processes need to become more efficient.•Machine learning is sufficiently mature for real-world use.•A machine learning classifier was built using data from Cochrane Crowd.•It was calibrated to achieve very high recall.•It is now live and in use in Cochrane review production systems.

We aimed to review how ‘Risk of Bias In Non-randomized Studies–of Interventions’ (ROBINS-I), a Cochrane risk of bias assessment tool, has been used in recent systematic reviews. Database and citation searches were conducted in March 2020 to identify recently published reviews using ROBINS-I. Reported ROBINS-I assessments and data on how ROBINS-I was used were extracted from each review. Methodological quality of reviews was assessed using AMSTAR 2 (‘A MeaSurement Tool to Assess systematic Reviews’). Of 181 hits, 124 reviews were included. Risk of bias was serious/critical in 54% of assessments on average, most commonly due to confounding. Quality of reviews was mostly low, and modifications and incorrect use of ROBINS-I were common, with 20% reviews modifying the rating scale, 20% understating overall risk of bias, and 19% including critical-risk of bias studies in evidence synthesis. Poorly conducted reviews were more likely to report low/moderate risk of bias (predicted probability 57% [95% CI: 47–67] in critically low-quality reviews, 31% [19–46] in high/moderate-quality reviews). Low-quality reviews frequently apply ROBINS-I incorrectly, and may thus inappropriately include or give too much weight to uncertain evidence. Readers should be aware that such problems can lead to incorrect conclusions in reviews.

This umbrella review was conducted to summarize the evidence and qualify the methodological quality of SR and SRMA published on small-sided games in team ball sports. A systematic review of Web of Science, PubMed, Cochrane Library, Scopus, and SPORTDiscus databases was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. From the 176 studies initially identified, 12 (eight SR and four SRMA) were fully reviewed, and their outcome measures were extracted and analyzed. Methodological quality (with the use of AMSTAR-2) revealed that seven reviews had low quality and five had critically low quality. Two major types of effects of SSGs were observed: (i) short-term acute effects and (ii) long-term adaptations. Four broad dimensions of analysis were found: (i) physiological demands (internal load); (ii) physical demands (external load) or fitness status; (iii) technical actions; and (iv) tactical behavior and collective organization. The psychological domain was reduced to an analysis of enjoyment. The main findings from this umbrella review revealed that SSGs present positive effects in improving aerobic capacity and tactical/technical behaviors, while neuromuscular adaptations present more heterogeneous findings. Factors such as sex, age group, expertise, skill level, or fitness status are also determinants of some acute effects and adaptations. The current umbrella review allowed to identify that most of the systematic review and meta-analysis conducted in SSGs presents low methodological quality considering the standards. Most of the systematic reviews included in this umbrella revealed that task constraints significantly change the acute responses in exercise, while SSGs are effective in improving aerobic capacity. Future original studies in this topic should improve the methodological quality and improve the experimental study designs for assessing changes in tactical/technical skills.

Background Systematic review with meta-analysis integrates findings from multiple studies, offering robust conclusions on treatment effects and guiding evidence-based medicine. However, the process is often hampered by challenges such as inconsistent data reporting, complex calculations, and time constraints. Researchers must convert various statistical measures into a common format, which can be error-prone and labor-intensive without the right tools. Implementation Meta-Analysis Accelerator was developed to address these challenges. The tool offers 21 different statistical conversions, including median & interquartile range (IQR) to mean & standard deviation (SD), standard error of the mean (SEM) to SD, and confidence interval (CI) to SD for one and two groups, among others. It is designed with an intuitive interface, ensuring that users can navigate the tool easily and perform conversions accurately and efficiently. The website structure includes a home page, conversion page, request a conversion feature, about page, articles page, and privacy policy page. This comprehensive design supports the tool’s primary goal of simplifying the meta-analysis process. Results Since its initial release in October 2023 as Meta Converter and subsequent renaming to Meta-Analysis Accelerator, the tool has gained widespread use globally. From March 2024 to May 2024, it received 12,236 visits from countries such as Egypt, France, Indonesia, and the USA, indicating its international appeal and utility. Approximately 46% of the visits were direct, reflecting its popularity and trust among users. Conclusions Meta-Analysis Accelerator significantly enhances the efficiency and accuracy of meta-analysis of systematic reviews by providing a reliable platform for statistical data conversion. Its comprehensive variety of conversions, user-friendly interface, and continuous improvements make it an indispensable resource for researchers. The tool’s ability to streamline data transformation ensures that researchers can focus more on data interpretation and less on manual calculations, thus advancing the quality and ease of conducting systematic reviews and meta-analyses.

The use of the four-part PICO model to facilitate search strategy development for a precise answer is recommended for structuring searches for systematic reviews. Existing guidelines generally recommend that a search strategy should include the population, intervention(s), and types of study design. Consequently, comparison and outcome are not recommended as a part of the search strategy. There is evidence that comparison and particularly outcome is not represented in enough detail, but this needs to be confirmed. The present study examines the presence of PICO elements in the records in two commonly used databases for health sciences research: Embase and PubMed. We examine the field of upper GI and pancreatic diseases as well as the field of pregnancy and childbirth by extracting the included studies as well as the related PICO elements from a random selection of Cochrane reviews within these two areas. We find that the PICO elements C and O had a lower retrieval potential across the two Cochrane groups and databases also when combining text words and subject headings. In particular, we find a lower retrieval when searching for both primary and secondary outcomes. Our results support the existing recommendation not to search for outcomes. •The PICO model is often used to develop the search strategy for a systematic review.•Existing guidelines generally recommend that a search strategy should include the population, intervention(s), and types of study design.•This study substantiates the assumption that when using PICO as a search strategy tool, some of the PICO elements will result in a considerably lower recall.•In particular, including outcomes as a search element will reduce recall as outcome is the element with the smallest retrieval potential of the four PICO elements and thus the results support the recommendation not to search for outcome.

Systematic reviews and meta-analyses are proliferating as they are an important building block to inform evidence-based guidelines and decision-making. Enforcement of best practice in clinical trials is firmly on the research agenda of good clinical practice, but there is less clarity as to how evidence syntheses that combine these studies can be influenced by bad practice. Our aim was to conduct a living systematic review of articles that highlight flaws in published systematic reviews to formally document and understand these problems. We conducted a comprehensive assessment of all literature examining problems, which relate to published systematic reviews. The first iteration of our living systematic review (https://systematicreviewlution.com/) has found 485 articles documenting 67 discrete problems relating to the conduct and reporting of systematic reviews which can potentially jeopardize their reliability or validity. Many hundreds of articles highlight that there are many flaws in the conduct, methods, and reporting of published systematic reviews, despite the existence and frequent application of guidelines. Considering the pivotal role that systematic reviews have in medical decision-making due to having apparently transparent, objective, and replicable processes, a failure to appreciate and regulate problems with these highly cited research designs is a threat to credible science. [Display omitted] •Living systematic review to organize and understand problems with published systematic reviews.•Comprehensive searches between 2000 and 2022 identified 485 included articles pertaining to 67 discrete problems with systematic reviews.•Problems with systematic reviews relate to their ability to be comprehensive, rigorous, transparent, and objective.•Not all problems are covered by existing systematic review guidelines and methods.•This living research aims to be a learning resource to improve the reliability and validity of future systematic reviews.