Conflict of interest disclosure by US cardiothoracic surgeons
Bibliographic record
Abstract
BACKGROUND: Surgeon-industry collaboration is a key driver of advancement in surgical technology and practice. Disclosures of financial relationships between investigators and industries are important to ensure transparent and critical evaluation of literature. METHODS: All American cardiothoracic (CT) surgeons who published in three major CT surgery journals in 2019 were identified. Whether these surgeons disclosed any conflicts of interest was recorded and compared to actual payments received within 5 years of publication as reported by the Centers for Medicare and Medicaid Services data. RESULTS: In the study period, there were 1079 unique manuscripts involving 885 American CT surgeons as authors, which combined for 2719 author instances. Of these, 96.2% of authors (851 of 885) received payments from companies. The authors who received payments produced 2651 author instances (97.4%). Financial disclosure was reported in only 11.4% (301 of 2651) of these instances. In total, 851 surgeons received more than $187 million over 5 years, with the highest-paid surgeon receiving an average of over $5.9 million per year. The largest individual payments were from "Associated Research Funding," with over $115 million being paid to 277 surgeons over 5 years. The top paying company issued over $96.5 million to American CT surgeons over 5 years. CONCLUSIONS: Nearly all the reviewed publications in three top CT surgery journals were by surgeons who received payments from companies, but very few of these payments were recorded as potential conflicts of interest. A more consistent and robust policy of COI disclosure is needed to reduce perceptions of bias.
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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 | 0.015 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.054 | 0.011 |
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".