Characteristics of physicians receiving large payments from pharmaceutical companies and the accuracy of their disclosures in publications: an observational study
Bibliographic record
Abstract
BACKGROUND: Financial relationships between physicians and industry are extensive and public reporting of industry payments to physicians is now occurring. Our objectives were to describe physician recipients of large total payments from these seven companies, and to examine discrepancies between these payments and conflict of interest (COI) disclosures in authors' concurrent publications. METHODS: The investigative journalism organization, ProPublica, compiled the Dollars for Docs database of payments to individuals from publically available data from seven US pharmaceutical companies during the period 2009 to 2010. We examined the cohort of 373 physicians in this database who each received USD $100,000 or more in the reporting period 2009 to 2010. RESULTS: These physicians received a total of $52,600,624 during this period (mean payment per physician $141,020). The predominant specialties were internal medicine and psychiatry. 147 of these physicians authored a total of 134 publications in the first quarter of 2011 and 77% (103) of these publications provided a COI disclosure. 69% of the 103 publications did not contain disclosures of the payment listed in the Dollars for Docs database. CONCLUSIONS: With increased public reporting of industry payments to physicians, it is apparent that large sums are being paid for services such as consulting and peer education. In over two-thirds of publications where COI disclosures were provided, the disclosures by physician authors did not include industry payments that were documented in the Dollars for Docs database.
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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 | 0.005 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".