Developing the RIGHT-COI&F extension for the reporting conflicts of interest and funding in practice guidelines: study protocol
Bibliographic record
Abstract
Background: Conflicts of interest (COI) and funding may influence the development of practice guidelines, but there are no internationally endorsed guidelines specifically focusing on the reporting on issues related to COI and funding in practice guidelines. Our aim is to develop an extension of the essential Reporting Items of Practice Guidelines in Healthcare (RIGHT) for COIs and Funding in practice guidelines (i.e., RIGHT-COI&F). Methods: We will follow the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) network's toolkit for developing a reporting guideline in six stages: (I) identifying the need for the extension; (II) registering the project and setting up working groups; (III) collecting the initial items; (IV) reaching consensus on the items to be included; (V) revision and formulation of the final checklist; and (VI) dissemination and implementation. We intend to form a multidisciplinary international team of experts to collect and evaluate the items and plan to complete the full reporting guideline in about 2 years. Discussion: The RIGHT-COI&F statement will help guideline developers improve their reporting of issues related to COIs and funding, and subsequently improve the reporting quality of their guidelines. Journals editors, guideline users and evaluators will benefit from a more complete and transparent reporting of COI. Trial Registration: We have registered the protocol on the EQUATOR network (https://www.equator-network.org/library/reporting-guidelines-under-development/reporting-guidelines-under-development-for-other-study-designs/#RIGHT-COI).
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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 | 0.226 | 0.344 |
| Meta-epidemiology (narrow) | 0.002 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.089 | 0.030 |
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".