Reporting guideline for overviews of reviews of healthcare interventions: The Preferred Reporting Items for Overviews of Reviews (PRIOR) statement
Bibliographic record
Abstract
The publication of systematic reviews has rapidly increased making it challenging to remain apprised of and interpret evidence from their growing number. A newer form of evidence synthesis, overview of reviews, synthesizes evidence from multiple systematic reviews. Authors would benefit from evidence- and consensus-based guidance for the complete and transparent reporting of overviews of reviews; in turn this will improve their reproducibility, trustworthiness, and usefulness for readers and end users (e.g., healthcare providers, healthcare decision-makers, policy-makers, patients/public).The PRIOR statement provides an evidence-based reporting guideline developed using established, rigorous methods that involved a four-stage process (project launch, evidence reviews, modified Delphi exercise, development of the reporting guideline) and an international stakeholder group representing varied experiences (e.g., authors, peer reviewers, editors, readers) and roles (e.g., patients/public, researchers, statisticians, librarians, healthcare professionals, policymakers). The PRIOR statement includes: a checklist with 27 main items that cover all steps and considerations involved in planning and conducting an overview of reviews of healthcare interventions; an explanation and elaboration document with rationale, essential elements, additional elements, and example for each item; and a flow diagram.
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 | 0.288 | 0.477 |
| Meta-epidemiology (narrow) | 0.006 | 0.009 |
| Meta-epidemiology (broad) | 0.012 | 0.026 |
| Bibliometrics | 0.023 | 0.028 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.012 | 0.008 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.023 | 0.016 |
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".