Preferred Reporting Items for Overviews of Reviews (PRIOR): a protocol for development of a reporting guideline for overviews of reviews of healthcare interventions
Bibliographic record
Abstract
BACKGROUND: Overviews of reviews (i.e., overviews) compile information from multiple systematic reviews to provide a single synthesis of relevant evidence for healthcare decision-making. Despite their increasing popularity, there are currently no systematically developed reporting guidelines for overviews. This is problematic because the reporting of published overviews varies considerably and is often substandard. Our objective is to use explicit, systematic, and transparent methods to develop an evidence-based and agreement-based reporting guideline for overviews of reviews of healthcare interventions (PRIOR, Preferred Reporting Items for Overviews of Reviews). METHODS: We will develop the PRIOR reporting guideline in four stages, using established methods for developing reporting guidelines in health research. First, we will establish an international and multidisciplinary expert advisory board that will oversee the conduct of the project and provide methodological support. Second, we will use the results of comprehensive literature reviews to develop a list of prospective checklist items for the reporting guideline. Third, we will use a modified Delphi exercise to achieve a high level of expert agreement on the list of items to be included in the PRIOR reporting guideline. We will identify and recruit a group of up to 100 international experts who will provide input into the guideline in three Delphi rounds: the first two rounds will occur via online survey, and the third round will occur during a smaller (8 to 10 participants) in-person meeting that will use a nominal group technique. Fourth, we will produce and publish the PRIOR reporting guideline. DISCUSSION: A systematically developed reporting guideline for overviews could help to improve the accuracy, completeness, and transparency of overviews. This, in turn, could help maximize the value and impact of overviews by allowing more efficient interpretation and use of their research 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 | 0.402 | 0.607 |
| Meta-epidemiology (narrow) | 0.008 | 0.011 |
| Meta-epidemiology (broad) | 0.015 | 0.021 |
| Bibliometrics | 0.024 | 0.032 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.013 | 0.023 |
| Insufficient payload (model declined to judge) | 0.053 | 0.025 |
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".