Women in cardiac surgery: a global workforce analysis
Bibliographic record
Abstract
OBJECTIVES: Cardiac surgery remains one of the most gender-imbalanced surgical specialties. Women constitute 6-11% of the North American workforce, while other regional data are scarce. Despite the acknowledged under-representation of women in cardiac surgery globally and evidence that surgeon-patient gender concordance enhances postoperative outcomes, precise figures remain poorly defined. Herein, we provide the 1st global quantification of women cardiac surgeons (WCS) and explore correlates of workforce diversity. METHODS: The Cardiothoracic Surgery Network database was queried for cardiac surgeons within each country and cross-validated with external sources. Profile pronouns and the genderize.io application determined surgeon sex. Data were stratified by country, geographical region and national income group, and correlation analyses with socioeconomic and gender parity metrics were performed. RESULTS: Women constitute 8.0% (1178/14 651) of the international cardiac surgical workforce, with a median of 0.00 WCS per million women (interquartile range: 0.00-0.09). North America (11.4%) and Europe (10.3%) lead regional representation, while East Asia (2.9%) and the Middle East (1.7%) rank lowest. High-income countries (9.9%) have double the proportion of WCS as low- and middle-income countries (4.8%), with a notable absence among low-income countries. Female representation correlates with Gross National Income per capita (τ = 0.39), the Global Gender Gap Index (τ = 0.26) and health expenditure (τ = 0.26). CONCLUSIONS: Improving female representation in cardiac surgery is essential to advancing social justice and overall patient care. Yet, WCS remain a minority worldwide, with the most pronounced disparities in low- and middle-income countries and regions with low Gross National Income, Global Gender Gap Index and health expenditure. Confronting these inequities will require targeted mentorship efforts and addressing country-specific entry barriers, necessitating further research into the unique factors influencing women in low- and middle-income countries.
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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 | 0.059 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| 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) | 0.000 | 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".