Promoting gender diversity and ergonomic equity in the cardiac surgery operating room
Bibliographic record
Abstract
PURPOSE OF REVIEW: The operating room (OR) infrastructure and equipment such as gloves, were historically designed at a time when most surgeons were male. Today, there are increasing numbers of females in the OR and we should ensure that there is not a disproportionate risk of ergonomic stress and risk of work-related injuries. This review provides a perspective on the representation of female cardiac surgeons globally and examines the unique ergonomic challenges they may face. RECENT FINDINGS: Female cardiac surgeons represent approximately 17% of practitioners in our sample of cardiac surgery centers, underscoring significant underrepresentation. Female cardiac surgeons report higher incidences of work-related musculoskeletal injuries and ergonomic challenges compared to their male colleagues. This could negatively impact their physical health and performance. Studies further highlight the inadequacy of standardized surgical tools and workstations in accommodating sex-specific anthropometry, contributing to the disproportionate strain experienced by female surgeons. SUMMARY: To mitigate gender disparities in cardiac surgery, there is a need to optimize OR infrastructure and surgical instrumentation to accommodate sex-based anatomical differences. Implementing ergonomic solutions, such as adjustable workstations and gender-specific surgical tools, could reduce musculoskeletal injuries and improve overall surgeon performance. Addressing these disparities represents a critical step toward fostering an equitable and inclusive surgical workforce, enhancing both the health and career longevity of female cardiac surgeons.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".