Inadequate conflit of interest policies at most French teaching hospitals : a survey and web analysis
Bibliographic record
Abstract
Background. There are 32 teaching hospitals in France, including 30 University hospitals and 2 Regional teachinghospitals. Teaching hospitals have three roles: health care provision, training of healthcare professionals, and medicalresearch. These roles lead to frequent interactions with pharmaceutical and medical device companies, inevitably raisingmajor risks of conflicts of interests. Therefore, policies to manage conflict of interests (COI) are crucial. This study aimsto examine COI policies in French teaching hospitals..Methods. All French teaching hospitals (n=32) were included in this study. All hospitals websites were screened forinstitutional COI policies and curriculum on COI, using standardized keyword searches. More data were collected througha questionnaire addressed to each chief executive officer (CEO) of the teaching hospital. We used predefined criteria (n=20) inspired by similar surveys on COI policies in French, US and Canadian medical schools, with some additions toreflect the local hospital context. A global score for each hospital, ranging from 0 to 58 (higher scores denoting strongerpolicies) was calculated by summing points obtained for each criterion.Results. Three out of 32 (9%) CEOs replied to the questionnaire. All 32 hospitals had websites; 16 hospitals listed policiesor regulations on their websites or provided them on request. In December 2017, among the 32 hospitals, we foundthat 17 (53.1%) had rules and regulations for some items only, 4 (12.5%) have considered implementing a policy, two ofwhich (6.3%) have begun implementation. and 15 (46.9%) had no evidence of COI policies and a null score. The maximumglobal score was 24 out of 58, with a mean of 3.50 ± 5.72.Conclusion. This is the first systematic assessment of COI policies in teaching hospitals in France. Such policies areneeded to protect patients, clinicians and students from undue commercial influence. Despite public and political pressurefor better management of COI since France’s benfluorex (Mediator) scandal of 2010, few teaching hospitals haveimplemented comprehensive and protective policies. We hope that periodic ranking of hospitals will contribute to raiseawareness of the importance of COI policy and speed introduction.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".