Reporting Conflicts of Interest and Funding in Health Care Guidelines: The RIGHT-COI&F Checklist
Bibliographic record
Abstract
BACKGROUND: Conflicts of interest (COIs) of contributors to a guideline project and the funding of that project can influence the development of the guideline. Comprehensive reporting of information on COIs and funding is essential for the transparency and credibility of guidelines. OBJECTIVE: To develop an extension of the Reporting Items for practice Guidelines in HealThcare (RIGHT) statement for the reporting of COIs and funding in policy documents of guideline organizations and in guidelines: the RIGHT-COI&F checklist. DESIGN: The recommendations of the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) network were followed. The process consisted of registration of the project and setting up working groups, generation of the initial list of items, achieving consensus on the items, and formulating and testing the final checklist. SETTING: International collaboration. PARTICIPANTS: 44 experts. MEASUREMENTS: Consensus on checklist items. RESULTS: The checklist contains 27 items: 18 about the COIs of contributors and 9 about the funding of the guideline project. Of the 27 items, 16 are labeled as policy related because they address the reporting of COI and funding policies that apply across an organization's guideline projects. These items should be described ideally in the organization's policy documents, otherwise in the specific guideline. The remaining 11 items are labeled as implementation related and they address the reporting of COIs and funding of the specific guideline. LIMITATION: The RIGHT-COI&F checklist requires testing in real-life use. CONCLUSION: The RIGHT-COI&F checklist can be used to guide the reporting of COIs and funding in guideline development and to assess the completeness of reporting in published guidelines and policy documents. PRIMARY FUNDING SOURCE: The Fundamental Research Funds for the Central Universities of China.
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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 | 0.293 | 0.685 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.017 | 0.008 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".