Protocol for the development of reporting guidance for interest-holder engagement in practice guidelines: the RIGHT-MuSE checklist
Bibliographic record
Abstract
Background: Guideline developers have increasingly engaged various groups of interest-holders in different stages of guideline development. However, reporting on interest-holder engagement in practice guidelines often lacks transparency. Therefore, the RIGHT (Reporting Items of Practice Guidelines in Healthcare) working group, in collaboration with the MuSE Consortium, plans to develop the RIGHT-MuSE extension. Objectives: This protocol aims to outline the steps for the detailed development of the RIGHT-MuSE checklist, as well as strategies for its effective development, dissemination, and application. Methods: We will follow the methods recommended by the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network, and build on the experience from the development of the original RIGHT statement and its extensions. The development process of RIGHT-MuSE will consist of twelve specific steps: 1. Identifying the need for the checklist; 2. Obtaining funding; 3. Drafting a protocol and registering the project; 4. Establishing the working groups; 5. Reviewing background work; 6. Generating an initial list of items; 7. Conducting consensus surveys; 8. Holding consensus meetings; 9. Drafting the final RIGHT-MuSE checklist; 10. Conducting a pilot test of the checklist; 11. Developing an explanatory document; and 12. Disseminating the checklist. Discussion: The RIGHT-MuSE checklist will provide guideline developers with guidance for the systematic, scientific and transparent reporting of interest-holder engagement in practice guidelines. Additionally, developers and implementers of the RIGHT-MuSE checklist will use this protocol as a reference to ensure that the checklist they develop and implement adheres to the highest standards of transparency and quality. By promoting active and meaningful engagement of interest-holders, the RIGHT-MuSE checklist aims to foster inclusive and people centered processes in healthcare.
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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 | 0.359 | 0.579 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.105 | 0.042 |
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".