Updating guidance for reporting systematic reviews: development of the PRISMA 2020 statement
Bibliographic record
Abstract
OBJECTIVES: To describe the processes used to update the PRISMA 2009 statement for reporting systematic reviews, present results of a survey conducted to inform the update, summarize decisions made at the PRISMA update meeting, and describe and justify changes made to the guideline. METHODS: We reviewed 60 documents with reporting guidance for systematic reviews to generate suggested modifications to the PRISMA 2009 statement. We invited 220 systematic review methodologists and journal editors to complete a survey about the suggested modifications. The results of these projects were discussed at a 21-member in-person meeting. Following the meeting, we drafted the PRISMA 2020 statement and refined it based on feedback from co-authors and a convenience sample of 15 systematic reviewers. RESULTS: The review of 60 documents revealed that all topics addressed by the PRISMA 2009 statement could be modified. Of the 110 survey respondents, more than 66% recommended keeping six of the original checklist items as they were and modifying 15 of them using wording suggested by us. Attendees at the in-person meeting supported the revised wording for several items but suggested rewording for most to enhance clarity, and further refinements were made over six drafts of the guideline. CONCLUSIONS: The PRISMA 2020 statement consists of updated reporting guidance for systematic reviews. We hope that providing this detailed description of the development process will enhance the acceptance and uptake of the guideline and assist those developing and updating future reporting guidelines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.451 | 0.759 |
| Meta-epidemiology (narrow) | 0.006 | 0.012 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.035 | 0.041 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.020 | 0.017 |
| Research integrity | 0.020 | 0.032 |
| Insufficient payload (model declined to judge) | 0.015 | 0.020 |
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".