A Reporting Tool for Adapted Guidelines in Health Care: The RIGHT-Ad@pt Checklist
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.
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 | 0.246 | 0.545 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.017 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".