Industry Relationships With Medical Oncologists: Who Are the High-Payment Physicians?
Bibliographic record
Abstract
PURPOSE: Many oncologists have relationships with industry. Previous work has shown that these payments are usually modest; however, there exist a subset of medical oncologists who receive more than $100,000 US dollars (USD) annually. Here, we describe the characteristics of these physicians. METHODS: This retrospective cohort study used the Open Payments data set to identify all US-based medical oncologists/hematologists who received $100,000+ USD in general payments linked to cancer medications in 2018. Open Payments and a web-based search were used to identify physician characteristics, demographics, research profile, and leadership positions. RESULTS: One hundred thirty-nine medical oncologists received > $100,000 USD in general payments. The median payment was $154,613 USD, and the total payment was $24.2 million USD. These high-payment physicians represent 1% of all US medical oncologists (N = 10,620) yet account for 37% of all industry payments in 2018. Sixty percent (84 of 139) and 21% (29 of 139) of these high-payment physicians hold hospital and specialty association leadership roles, respectively. One quarter (24%, 33 of 139) serve on journal editorial boards, and 10% (14 of 139) have authored clinical practice guidelines; 72% (100 of 139) hold faculty appointments. CONCLUSION: A small number of medical oncologists receive very high payments from the pharmaceutical industry. These physicians hold major leadership roles within oncology. Further work is needed to understand the extent to which these conflicts of interest may shape clinical practice and policy.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".