60 Years After the First Woman Cardiac Surgeon: We Still Need More Women in Cardiac Surgery
Bibliographic record
Abstract
In 1960, Dr Nina Starr Braunwald became the first woman to perform open heart surgery. Sixty years later, despite the fact that women outnumbered men in American medical school in 2017, men still dominate the field of cardiac surgery. Women surgeons remain underrepresented in cardiac surgery; 11% of practicing cardiac surgeons in Canada were women in 2015, and 6% of practicing adult cardiac surgeons in the US were women in 2019. Although women remain a minority in other surgical specialties also, cardiothoracic surgery remains one of the most unevenly-gender distributed specialties. Why are there so few women cardiac surgeons, and why does it matter? Evidence is emerging regarding the benefits of diversity for a variety of industries, including healthcare. In order to attract and retain the best talent, we must make the cardiac surgery environment more diverse, equitable, and inclusive. Some causes of perpetuation of the gender gap have been documented in the literature-these include uneven compensation and career advancement opportunities, outdated views on family dynamics, and disproportionate scrutiny of women surgeons, causing additional workplace frictions for women. Diversity is an organizational strength, and gender-diverse institutions are more likely to outperform their non-gender-diverse counterparts. Modifiable issues perpetuate the gender gap, and mentorship is key in helping attract, develop, and retain the best and brightest within cardiac surgery. Facilitating mentorship opportunities is key to reducing barriers and bridging the gap.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.053 | 0.018 |
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".