Protocol of reporting items for public versions of guidelines: the Reporting Tool for Practice Guidelines in Health Care—public versions of guidelines
Bibliographic record
Abstract
INTRODUCTION: Patient and public versions of guidelines (PVGs) can help with individual decision making and enhance the patient-clinician relationship by providing easily understandable and reliable information. An increasing number of guideline organisations are developing PVGs. However, the reporting of PVGs by different groups and organisations varies widely. This study aims to develop a reporting checklist for PVGs for healthcare. METHODS AND ANALYSIS: We will develop the PVG reporting checklist as an extension of the Reporting Tool for Practice Guidelines in Healthcare (RIGHT) statement. We will build on the methods recommended by the EQUATOR network, which is our starting point. We will conduct a literature review, establish an international multidisciplinary team, run a modified Delphi process to identify the reporting items and pilot test the draft reporting checklist. We plan to update the checklist every 3 years. ETHICS AND DISSEMINATION: Ethics approval and patient consent are not required since this study will not undertake any formal data collection involving humans or animals. The results of this protocol will be submitted to a peer-reviewed journal for publication. TRIAL REGISTRATION: We registered the protocol on the EQUATOR network (http://www.equator-network.org/library/reporting-guidelines-under-development/#84).
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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 | 0.429 | 0.698 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.010 | 0.022 |
| Insufficient payload (model declined to judge) | 0.052 | 0.033 |
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".