Nature and Trends in Personal Payments Made to the Respiratory Physicians by Pharmaceutical Companies in Japan between 2016 and 2019
Bibliographic record
Abstract
BACKGROUND: Financial relationships between healthcare professionals and pharmaceutical companies have historically caused conflicts of interest and unduly influenced patient care. However, little was known about such relationship and its effect in clinical practice among specialists in respiratory medicine. METHODS: Based on the retrospective analysis of payment data made available by all 92 pharmaceutical companies in Japan, this study evaluated the magnitude and trend of financial relationships between all board-certified Japanese respiratory specialists and pharmaceutical companies between 2016 and 2019. Magnitude and prevalence of payments for specialists were analyzed descriptively. The payment trends were assessed using the generalized estimating equations for the payment per specialist and the number of specialists with payments. RESULTS: Among all 7,114 respiratory specialists certified as of August 2021, 4,413 (62.0%) received a total of USD 53,547,391 and 74,195 counts from 72 (78.3%) pharmaceutical companies between 2016 and 2019. The median (interquartile range) 4-year combined payment values per specialist were USD 2,210 (USD 715-8,178). At maximum, one specialist received USD 495,332 personal payments over the 4 years. Both payments per specialist and number of specialists with payments significantly increased during the 4-year period, with 7.8% (95% CI: 5.5-9.8; p < 0.001) in payments and 1.5% (95% CI: 0.61-2.4; p = 0.001) in number of specialists with payments, respectively. CONCLUSION: The majority of respiratory specialists had increasingly received more personal payments from pharmaceutical companies for the reimbursement of lecturing, consulting, and writing between 2016 and 2019. These increasing financial relationships with pharmaceutical companies might cause conflicts of interest among respiratory physicians.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; 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".