Surgeon-industry conflict of interest: survey of opinions regarding industry-sponsored educational events and surgeon teaching
Bibliographic record
Abstract
OBJECT: Conflict of interest (COI) as it applies to medical education and training has become a source of considerable interest, debate, and regulation in the last decade. Companies often pay surgeons as faculty for educational events and often sponsor and give financial support to major professional society meetings. Professional medical societies, industry, and legislators have attempted to regulate potential COI without consideration for public opinion. The practice of evidence-based medicine requires the inclusion of patient opinion along with best available evidence and expert opinion. The primary goal of this study was to assess the opinion of the general population regarding surgeon-industry COI for education-related events. METHODS: A Web-based survey was administered, with special emphasis on the surgeon's role in industry-sponsored education and support of professional societies. A survey was constructed to sample opinions on reimbursement, disclosure, and funding sources for educational events. RESULTS: There were 501 completed surveys available for analysis. More than 90% of respondents believed that industry funding for surgeons' tuition and travel for either industry-sponsored or professional society educational meetings would either not affect the quality of care delivered or would cause it to improve. Similar results were generated for opinions on surgeons being paid by industry to teach other surgeons. Moreover, the majority of respondents believed it was ethical or had no opinion if surgeons had such a relationship with industry. Respondents were also generally in favor of educational conferences for surgeons regardless of funding source. Disclosures of a surgeon-industry relationship, especially if it involves specific devices that may be used in their surgery, appears to be important to respondents. CONCLUSIONS: The vast majority of respondents in this study do not believe that the quality of their care will be diminished due to industry funding of educational events, for surgeon tuition, and/or travel expenses. The results of this study should help form the basis of policy and continued efforts at surgeon-industry COI management.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".