P.119 Industry relationships with neurological surgery in the 2015 Open Payments Database
Bibliographic record
Abstract
Background: The 2013 Physician Payments Sunshine Act mandates that all US drug and device manufacturers disclose payments to physicians annually in the Open Payments Database (OPD). We aimed to determine the prevalence, magnitude and nature of these payments to neurological surgery in 2015. Methods: Records of payments to physicians identified by the ‘neurological surgery’ taxonomy code in 2015 were accessed via the OPD. The data were analyzed in terms of the type and amounts of payments, companies making payments, and in comparison to previous studies. Results: In 2015, 330 companies made 83,690 payments ($99,048,607) to 7,613 physicians. The mean payment ($13,010) was substantially greater than the median ($114). Royalties and licensing accounted for the largest proportion of total payment value (74.2%), but only 1.7% of the total number. Food and beverage payments were the most commonly reported transaction (75%), but only 2.5% of the total value. Neurological surgery had the second highest average total payment per physician of any specialty. Conclusions: The overall value of payments to the neurological surgery specialty is driven by a small number of payments that may represent appropriate compensation for novel device development. The OPD provides an opportunity for increased transparency and for the interpretation of research in light of potential conflicts of interest.
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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 | 0.003 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".