PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews
Bibliographic record
Abstract
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.Systematic reviews are essential for healthcare providers, policy makers, and other decision makers, who would otherwise be confronted by an overwhelming volume of research on which to base their decisions.To allow decision makers to assess the trustworthiness and applicability of review findings, reports of systematic reviews should be transparent and complete.Furthermore, such reporting should allow others to replicate or update reviews.The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement published in 2009 (hereafter referred to as PRISMA 2009) 1-12 was designed to help authors prepare transparent accounts of their reviews, and its recommendations have been widely endorsed and adopted.13 We have updated the PRISMA 2009 statement (to PRISMA 2020) to ensure currency and relevance and to reflect advances in systematic review methodology and terminology. Scope of this guidelineThe PRISMA 2020 statement has been designed primarily for systematic reviews of studies that evaluate the effects of health interventions, irrespective of the design of the included studies.However, the checklist items are applicable to reports of systematic reviews evaluating other non-health-related interventions (for example, social or educational interventions), and many items are applicable to systematic reviews with objectives other than evaluating interventions (such as evaluating aetiology, prevalence, or prognosis).PRISMA 2020 is intended for use in systematic reviews that include synthesis (such as pairwise metaanalysis or other statistical synthesis methods) or do not include synthesis (for example, because only one eligible study is identified).The PRISMA 2020 items are relevant for mixed-methods systematic reviews (which include quantitative and qualitative studies), but reporting guidelines addressing the presentation and synthesis of qualitative data should also be consulted.14 15 PRISMA 2020 can be used for original systematic reviews, updated systematic reviews, or continually updated ("living") systematic reviews.However, for updated and living systematic reviews, there may be some additional considerations that need to be addressed.Extensions to the PRISMA 2009 statement have been developed to guide reporting of network meta-analyses, 16 meta-analyses of individual participant data, 17 systematic reviews of harms, 18 systematic reviews of diagnostic test accuracy studies, 19 and scoping reviews 20 ; for these types of reviews we recommend authors report their review For numbered affiliations see end of the article.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.341 | 0.608 |
| Meta-epidemiology (narrow) | 0.008 | 0.016 |
| Meta-epidemiology (broad) | 0.013 | 0.033 |
| Bibliometrics | 0.022 | 0.031 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.012 | 0.012 |
| Research integrity | 0.014 | 0.033 |
| Insufficient payload (model declined to judge) | 0.093 | 0.062 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".