A Reporting Tool for Adapted Guidelines in Health Care: The RIGHT-Ad@pt Checklist
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Adaptation of existing guidelines can be an efficient way to develop contextualized recommendations. Transparent reporting of the adaptation approach can support the transparency and usability of the adapted guidelines. OBJECTIVE: To develop an extension of the RIGHT (Reporting Items for practice Guidelines in HealThcare) statement for the reporting of adapted guidelines (including recommendations that have been adopted, adapted, or developed de novo), the RIGHT-Ad@pt checklist. DESIGN: A multistep process was followed to develop the checklist: establishing a working group, generating an initial checklist, optimizing the checklist (through an initial assessment of adapted guidelines, semistructured interviews, a Delphi consensus survey, an external review, and a final assessment of adapted guidelines), and approval of the final checklist by the working group. SETTING: International collaboration. PARTICIPANTS: A total of 119 professionals participated in the development process. MEASUREMENTS: Participants' consensus on items in the checklist. RESULTS: The RIGHT-Ad@pt checklist contains 34 items grouped in 7 sections: basic information (7 items); scope (6 items); rigor of development (10 items); recommendations (4 items); external review and quality assurance (2 items); funding, declaration, and management of interest (2 items); and other information (3 items). A user guide with explanations and real-world examples for each item was developed to provide a better user experience. LIMITATION: The RIGHT-Ad@pt checklist requires further validation in real-life use. CONCLUSION: The RIGHT-Ad@pt checklist has been developed to improve the reporting of adapted guidelines, focusing on the standardization, rigor, and transparency of the process and the clarity and explicitness of adapted recommendations. PRIMARY FUNDING SOURCE: None.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it