The relation between publication rate and financial conflict of interest among physician authors of high-impact oncology publications: an observational study
Bibliographic record
Abstract
BACKGROUND: Despite the abundant research on financial conflict of interest regarding provider behaviour and the interpretation and results of research, little is known about the relation between these conflicts in academia and the trajectory of one's academic career. We performed a study to examine whether the presence of financial ties to drug makers among academics is associated with research productivity. METHODS: ) to identify physicians based in the United States who were first or last authors on original papers on hematologic or oncologic topics that appeared in 2015. We ascertained their publication history from Scopus and their personal and research payments from the Centers for Medicare & Medicaid Services' Open Payments Web site (2013-2015). The strength of association between general (personal) financial payments from 2013 to 2015 and publications from 2013 to 2016 was determined by multivariate regression. RESULTS: Our sample consisted of 435 physicians who had authored a median of 140 publications, earning a median h-index of 36 and a median of 5639 citations. The median total of general payments from 2013 to 2015 was US$3282 (range $0-$3.4 million), and the median amount of research payments was US$3500 (range $0-$23 million). General payments were associated with contemporary publications, with an increase of 1.99 papers (95% confidence interval [CI] 1.1 to 2.9) per $10 000 in payments. This association persisted in multivariate analysis after adjustment for prior publications, seniority and research payments (0.84 papers [95% CI 0.15 to 1.5] per $10 000 in payments). INTERPRETATION: The findings suggest that there is a positive association between personal payments from drug makers and publications, and that this association persists after adjustment for prior publications, time since medical school graduation and research payments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".