Payments by Drug and Medical Device Manufacturers to Society of Urologic Oncology Fellowship Program Directors
Bibliographic record
Abstract
INTRODUCTION: This study aims to characterize payments by drug and medical device manufacturers to current program directors (PDs) of the Society of Urologic Oncology (SUO)-accredited fellowship programs in the United States. METHODS: PDs were identified from SUO fellowship websites as of February 2024. Demographic data, educational background, and scholarly metrics were collected through an online search. Industry payments to SUO PDs from 2014 to 2023 were extracted from the Open Payments database. Descriptive statistics were used to summarize PD characteristics and industry payment details. Univariable linear regression was used to assess the association of PD characteristics or scholarly metrics with payments. RESULTS: Fifty-one PDs from 37 SUO fellowship programs were identified. PDs were predominantly men (94%) and mid career. In aggregate, over the study period, PDs received US dollars ($) 18,963,555 in industry payments over 10 years. Most payments were for associated research funding ($15,490,525, 81.6%; median [IQR] per PD recipient, $126,584 [$36,565-$706,516]; 1262 payments). General payments accounted for a total of $3,473,030 (18.3%; median [IQR] per PD, $10,345 [$2196-$49,180]). SUO PDs received $120,763 (0.6%) for education fees. No association was found between PD characteristics or research metrics and industry payments. CONCLUSIONS: PDs of SUO fellowships receive significant industry payments, surpassing those received by the average urologist. Most of these payments are allocated to research, with smaller proportions directed to general support and educational initiatives.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".