A cross‐sectional review of policies on conflicts of interest and funding in the development manuals of practice guidelines
Bibliographic record
Abstract
Abstract Importance Policies on conflicts of interest (COI) and funding are essential to reduce the risk of bias in the guideline development process. Objective To collate and review the content related to COI and funding policies from guideline development handbooks. Study design and setting We searched PubMed from its inception until September 10, 2021, websites of key guideline development organizations and Google for guideline development manuals that included COI or funding policies, and performed a cross‐sectional review. Results Fifty‐seven guideline development manuals were included. Amongst the 54 handbooks containing a COI policy, all required disclosure of interests. Nineteen (35.2%) manuals defined what constitutes a COI, and 52 (96.3%) specified who should disclose their interests. Thirty‐four (63.0%) manuals recommended an assessment of disclosed interests to determine whether a COI existed, and all of these specified who should perform this review. Thirty‐five (64.8%) manuals addressed the management of COI, of which 26 (74.3%) indicated who should manage COI and 29 (82.9%) reported specific management measures. Twenty‐eight (51.8%) manuals addressed the publication of COI, all recommending that these be publicly accessible. Of the 28 manuals that provided guidance on funding, eight (28.6%) required reporting of funding sources; 14 (50.0%) required that the guideline authors state that the funders' perspectives and interests did not affect the final recommendations; eight (28.6%) specified which kind of funding the guidelines should not accept; and five (17.9%) recommended that the role of funders be restricted. Conclusions Policies in guideline manuals report a variety of different elements related to COI and funding. However, a considerable part of the policies did not report precisely what constitutes a COI, the key steps for COI management, or address the sources, influence and acceptability of funding.
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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 | 0.164 | 0.582 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.031 | 0.030 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".