« Sunshine on Opinion Leaders » : étude rétrospective des liens d’intérêt financiers entre les leaders d’opinion médicaux et l’industrie pharmaceutique en France
Bibliographic record
Abstract
Objective: to investigate the nature, extent and evolution of financial relationships between opinion leaders (OL) or non-OL physicians and pharmaceutical and device companies in France. Design: retrospective and descriptive study. Participants: all medical doctors practicing in France, with a focus on 548 OL defined as board members of all the professional medical associations having published clinical practice guidelines in 2018 or 2019. These 99 associations were identified by the cross-checking of 3 different catalogs of French professional associations. Main outcome measures: the number and the total amount of gifts received by all the medical physicians and by the identified OL, year by year since 2014. The number and the amount of the 2 additional categories of payments available only since 2017 (i.e. remunerations and agreements), and the number of agreements without declared amount; described for all physicians and OL, year by year since 2017. Results: physicians had €818m ($936m, £741m) of gifts declared from 2014 to 2019. 83% of OL had such links of interest. The 548 identified OL represented 0.24% of physicians in France but received 1.5% of the total amount of gifts, i.e. €12.3m ($14m, £11m or €3,700 per capita per year). 1.2 million agreements (77%) had their amount hidden. Physicians had €125m ($143m and £114m) of agreements declared from 2017 to 2019. The 548 OL received 0.72% of the agreements and 2.5% of the value of the agreements, i.e. €3.1m ($3.6m, £2.8m) or €1,900 per capita per year ($2,200, £1,700). Physicians had €156m ($178m and 141m£) of remunerations declared from 2017 to 2019. The 548 identified OL received 4.4% of the total value of remunerations to physicians, i.e. €6.8m ($7.8m, £6.2m) or €4,100 per capita per year ($4,800, £3,700). Almost every professional medical associations (99%) had in their board at least one OL with a financial tie. Conclusion: financial relationships between OL and the industry in France are extensive, OL have much more financial ties than non-OL practitioners. The main limit of this study arises from the quality of information provided on the French Transparency in Healthcare database: more than three-quarters of agreements have their amount hidden despite the obligation to indicate it. The quantum of payments raises questions about the necessary independence of those who write clinical guidelines, adding weight to calls for policy reform.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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".