The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
Bibliographic record
Abstract
Background: 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 last decade, there have been many advances in systematic review methodology and terminology, which have necessitated an update to the guideline.Objectives: To develop the PRISMA 2020 statement for reporting systematic reviews.Methods: We reviewed 60 documents with reporting guidance for systematic reviews to generate suggested modifications to the PRISMA 2009 statement. We sought feedback on the suggested modifications through an online survey of 110 systematic review methodologists and journal editors. The results of the review and survey were discussed at a 21-member in-person meeting. Following the meeting, drafts of the PRISMA 2020 checklist, abstract checklist, explanation and elaboration and flow diagram were generated and refined iteratively based on feedback from co-authors and a convenience sample of 15 systematic reviewers.Results: In this statement paper, 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 checklist 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. The PRISMA 2020 statement replaces the 2009 statement.Conclusions: The PRISMA 2020 statement is intended to facilitate transparent, complete and accurate reporting of systematic reviews. Improved reporting should benefit users of reviews, including guideline developers, policy makers, health care providers, patients and other stakeholders. In order to achieve this, we encourage authors, editors and peer-reviewers to adopt the guideline.
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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 | 0.362 | 0.600 |
| Meta-epidemiology (narrow) | 0.004 | 0.007 |
| Meta-epidemiology (broad) | 0.010 | 0.019 |
| Bibliometrics | 0.026 | 0.027 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.012 | 0.009 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.024 | 0.014 |
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".