Physician-industry conflict of interest: public opinion regarding industry-sponsored research
Bibliographic record
Abstract
OBJECT: The nature of physician-industry conflict of interest (COI) has become a source of considerable concern, but is often not discussed in the research setting. With reduced funding available from government and nonprofit sources, industry support has enthusiastically grown, but along with this comes the potential for COI that must be regulated. In this era of shared decision making in health care, society must have input into this regulation. The purpose of this study was to assess the opinions of a North American population sample on COI regarding industry-funded research and to analyze population subgroups for trends. METHODS: A survey was developed for face and content validity, underwent focus group evaluation for clarity and bias reduction, and was administered via the World Wide Web. Demographic and general survey results were summarized as a percentage for each answer, and subgroup analysis was done using logistic regression. Generalizability of the sample to the US population was also assessed. RESULTS: Of 541 surveys, 40 were excluded due to missing information, leaving 501 surveys for analysis. The sample population was composed of more females, was older, and was more educated than a representative cross-section of the American population. Respondents support multidisciplinary surgeon-industry COI regulation and trust doctors and their professional societies the most to head this effort. Respondents trust government officials and company representatives the least with respect to regulation of COI. Most respondents feel that industry-sponsored research can involve physicians and be both objective and beneficial to patients. CONCLUSIONS: Most respondents in this study felt that surgeons should be involved in industry-sponsored research and that more research, regardless of funding source, will ultimately benefit patients. The majority of respondents distrust government or industry to regulate COI. The development of evidence-based treatment recommendations requires the inclusion of patient preference. The authors encourage regulatory bodies to follow suit and include society's perspective on regulation of COI in research.
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.015 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".