Eponymously named surgical instruments and gender: why representation matters
Bibliographic record
Abstract
Women continue to be under-represented in most surgical specialties, especially in academic hospitals. Historically, most surgical instruments are named for the surgeon who developed or invented them. A review of surgical instruments was completed to better understand the impact of women innovators in surgery. Eponymous instrument names were cross-referenced to the surgeon for whom they were named through a review of historical texts, medical journals, and online instrument catalogues; an interview was also conducted. Of 458 eponymous instrument names, only three were connected to women: spine surgeon Dr. Nancy Epstein, and ophthalmologists Dr. Bonnie Henderson and Dr. Sheri Rowen. Dr. Sheri Rowen was interviewed to discuss her experience developing new surgical instruments and her career as a female surgeon. This interview highlighted the importance of same-gender role models in surgery, which is supported by the literature; having female surgeon role models is associated with a greater interest in a surgical career for female medical students. Gender-based discrimination has also been shown in the literature to be a barrier against women in surgery. A discussion of opportunities for improving the representation of women in surgery is presented: medical education departments should improve female surgeon representation through lectures, conferences, and meetings; schools should also provide female surgeon mentorship for female medical students.
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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 | 0.016 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".