The reporting checklist for public versions of guidelines: RIGHT-PVG
Bibliographic record
Abstract
Abstract Background Public or patient versions of guidelines (PVGs) are derivative documents that “translate” recommendations and their rationale from clinical guidelines for health professionals into a more easily understandable and usable format for patients and the public. PVGs from different groups and organizations vary considerably in terms of quality of their reporting. In order to address this issue, we aimed to develop a reporting checklist for developers of PVGs and other potential users. Methods First, we collected a list of potential items through reviewing a sample of PVGs, existing guidance for developing and reporting PVGs or other similar evidence-based patient tools, as well as qualitative studies on original studies of patients’ needs about the content and/or reporting of information in PVGs or similar evidence-based patient tools. Second, we conducted a two-round Delphi consultation to determine the level of consensus on the items to be included in the final reporting checklist. Third, we invited two external reviewers to provide comments on the checklist. Results We generated the initial list of 45 reporting items based on a review of a sample of 30 PVGs, four PVG guidance documents, and 46 relevant studies. After the two-round Delphi consultation, we formed a checklist of 17 items grouped under 12 topics for reporting PVGs. Conclusion The RIGHT-PVG reporting checklist provides an international consensus on the important criteria for reporting PVGs.
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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 | 0.335 | 0.633 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.015 | 0.008 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.026 | 0.015 |
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".