Intellectual conflicts of interest among cardiology and pulmonology clinical practice guidelines
Bibliographic record
Abstract
BACKGROUND: Intellectual conflicts of interest (COI), like financial COI, may threaten the validity and trustworthiness of clinical practice guidelines (CPGs). However, comparatively little is known about intellectual COI in CPGs. This study sought to estimate the prevalence of intellectual COI and corresponding management strategies among cardiology and pulmonology CPGs. METHODS: We conducted a retrospective document review of CPGs published by cardiology or pulmonology professional societies from the United States, Canada, or Europe from 2018 to 2019 available via the Emergency Care Research Institute, Guidelines International Network, or Medscape databases. We assessed the percentage of authors with an intellectual COI, defined as i) authorship on a study reviewed by the CPG, ii) authorship of a prior editorial related to a CPG recommendation, or iii) authorship of a prior related CPG. Management strategies assessed included use of GRADE methodology, inclusion of a methodologist, and recusals due to intellectual COI. Outcomes were assessed overall and compared between cardiology and pulmonology CPGs. RESULTS: Among the 39 CPGs identified (14 cardiology, 25 pulmonology), there were a total of 737 authors, of whom 473 (64%) had at least one intellectual COI. Among all CPGs, a median of 67% (Interquartile Range 50%-76%) of authors had at least one intellectual COI, and COI was more prevalent among cardiology compared with pulmonology CPGs (84% vs 57%, p<0.001). There was variable use of management strategies among the CPGs, including use of GRADE methodology (64% of CPGs), inclusion of a methodologist (49%), and recusals due to intellectual COI (0%). CONCLUSION: Intellectual conflicts of interest appear to be highly prevalent and under-reported among cardiology and pulmonology CPGs, which may threaten their validity. Greater attention to and improved management of intellectual COI by CPG-producing organizations is needed.
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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 | 0.096 | 0.514 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".