Prevalence of financial conflicts of interest among panel members producing clinical practice guidelines in Canada and United States: cross sectional study
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of financial conflicts of interest among members of panels producing clinical practice guidelines on screening, treatment, or both for hyperlipidaemia or diabetes. DESIGN: Cross sectional study. SETTING: Relevant guidelines published by national organisations in the United States and Canada between 2000 and 2010. PARTICIPANTS: Members of guideline panels. MAIN OUTCOME MEASURES: Prevalence of financial conflicts of interest among members of guideline panels and chairs of panels. RESULTS: Fourteen guidelines met our search criteria, of which five had no accompanying declaration of conflicts of interest by panel members. 288 panel members had participated in the guideline development process. Among the 288 panel members, 138 (48%) reported conflicts of interest at the time of the publication of the guideline and 150 (52%) either stated that they had no such conflicts or did not have an opportunity to declare any. Among 73 panellists who formally declared no conflicts, 8 (11%) were found to have one or more. Twelve of the 14 guideline panels evaluated identified chairs, among whom six had financial conflicts of interest. Overall, 150 (52%) panel members had conflicts, of which 138 were declared and 12 were undeclared. Panel members from government sponsored guidelines were less likely to have conflicts of interest compared with guidelines sponsored by non-government sources (15/92 (16%) v 135/196 (69%); P<0.001). CONCLUSIONS: The prevalence of financial conflicts of interest and their under-reporting by members of panels producing clinical practice guidelines on hyperlipidaemia or diabetes was high, and a relatively high proportion of guidelines did not have public disclosure of conflicts of interest. Organisations that produce guidelines should minimise conflicts of interest among panel members to ensure the credibility and evidence based nature of the guidelines' content.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".