International Medical Graduates in Academic Cardiothoracic Surgery
Bibliographic record
Abstract
OBJECTIVE: To assess the research productivity, career advancement, grant funding, and scholarly impact of international medical graduates (IMGs) in academic cardiothoracic (CT) surgery. BACKGROUND: Physician shortages undermine patient care and risk exacerbating inequities, especially in CT surgery, which may lose a quarter of its workforce by 2050-the most substantial reduction in surgery. IMGs could help alleviate these shortages, but there is limited data about their academic experiences. METHODS: All CT surgeons (n = 1065) at accredited U.S. CT surgery training centers in 2020 were included. IMGs were defined as surgeons who completed medical school outside the United States and Canada, per the Association of American Medical Colleges. Educational and professional backgrounds were recorded from publicly available sources. RESULTS: Of academic CT surgeons, 24.0% were IMGs. These surgeons started as attendings in later years (2012 vs 2005, P < 0.001) than non-IMGs. In unadjusted analyses, IMGs had lower publication counts and H-index, as well as lower likelihood of R01 funding and full professor attainment. Matching by attending start year, propensity score analysis created 2 groups of 254 surgeons: both IMGs and non-IMGs had similar publication counts (45.0 vs 45.0, P = 0.98), H-index (10.5 vs 11.0, P = 0.61), R01 funding rates (4.3% vs 5.1%, P = 0.83), and full professor attainment (24.8% vs 20.5%, P = 0.45). CONCLUSIONS: IMGs represent a more junior cohort of surgeons but contribute significantly to the CT surgery workforce, with comparable academic success. Policy efforts to streamline IMGs' path toward U.S. practice could help alleviate surgical shortages while enhancing diversity and strengthening academia.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".