Gender disparities in industry compensation and research payments among neurointerventional surgeons in the USA
Bibliographic record
Abstract
BACKGROUND: The purpose of this study is to examine the presence of gender disparity represented by industry payments and research funding within the field of interventional neuroradiology. METHODS: Payment information was collected using the Centers for Medicare and Medicaid Services Open Payment database for the year 2019. Kruskal-Wallis tests were used to analyze differences in annual compensation based on sex in $US, while controlling for geographic factors, academic rank, and h-index. A sample t-test was performed to look at gender differences in h-indexes. RESULTS: The study cohort was comprised of 893 interventional neuroradiologists, 73 (8.2%) of which were female. Of the $48889.20 in mean annual payments reported in the database, $5847.13 (11.2%) went to female interventional neuroradiologists (P<0.05). The significant difference in compensation between male and female neuroradiologists was evident after controlling for state-level variance and academic position. There was a statistically significant difference in total reimbursement (P<0.001), research (P<0.001), consulting (P<0.04), food and beverage (P<0.02), and compensation for services other than consulting between males and females (P<0.02). A statistically significant difference was found for h-index based on gender (males=16.7, females=10.1; P<0.001). CONCLUSIONS: Our findings indicate that in the field of interventional neuroradiology, females receive less research funding and private industry compensation, have lower h-indexes, and are less likely to occupy the highest academic positions. The difference in funding did not differ when accounting for geographic state of practice and academic rank. Future studies should work to identify potential contributory factors of these trends.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".