Reporting guideline for overviews of reviews of healthcare interventions: development of the PRIOR statement
Bibliographic record
Abstract
OBJECTIVE: To develop a reporting guideline for overviews of reviews of healthcare interventions. DESIGN: Development of the preferred reporting items for overviews of reviews (PRIOR) statement. PARTICIPANTS: Core team (seven individuals) led day-to-day operations, and an expert advisory group (three individuals) provided methodological advice. A panel of 100 experts (authors, editors, readers including members of the public or patients) was invited to participate in a modified Delphi exercise. 11 expert panellists (chosen on the basis of expertise, and representing relevant stakeholder groups) were invited to take part in a virtual face-to-face meeting to reach agreement (≥70%) on final checklist items. 21 authors of recently published overviews were invited to pilot test the checklist. SETTING: International consensus. INTERVENTION: Four stage process established by the EQUATOR Network for developing reporting guidelines in health research: project launch (establish a core team and expert advisory group, register intent), evidence reviews (systematic review of published overviews to describe reporting quality, scoping review of methodological guidance and author reported challenges related to undertaking overviews of reviews), modified Delphi exercise (two online Delphi surveys to reach agreement (≥70%) on relevant reporting items followed by a virtual face-to-face meeting), and development of the reporting guideline. RESULTS: From the evidence reviews, we drafted an initial list of 47 potentially relevant reporting items. An international group of 52 experts participated in the first Delphi survey (52% participation rate); agreement was reached for inclusion of 43 (91%) items. 44 experts (85% retention rate) completed the second Delphi survey, which included the four items lacking agreement from the first survey and five new items based on respondent comments. During the second round, agreement was not reached for the inclusion or exclusion of the nine remaining items. 19 individuals (6 core team and 3 expert advisory group members, and 10 expert panellists) attended the virtual face-to-face meeting. Among the nine items discussed, high agreement was reached for the inclusion of three and exclusion of six. Six authors participated in pilot testing, resulting in minor wording changes. The final checklist includes 27 main items (with 19 sub-items) across all stages of an overview of reviews. CONCLUSIONS: PRIOR fills an important gap in reporting guidance for overviews of reviews of healthcare interventions. The checklist, along with rationale and example for each item, provides guidance for authors that will facilitate complete and transparent reporting. This will allow readers to assess the methods used in overviews of reviews of healthcare interventions and understand the trustworthiness and applicability of their findings.
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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 | 0.504 | 0.649 |
| Meta-epidemiology (narrow) | 0.006 | 0.011 |
| Meta-epidemiology (broad) | 0.013 | 0.026 |
| Bibliometrics | 0.030 | 0.025 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.015 | 0.016 |
| Research integrity | 0.017 | 0.021 |
| Insufficient payload (model declined to judge) | 0.009 | 0.013 |
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".