Association between anaesthesia–surgery team sex diversity and major morbidity
Bibliographic record
Abstract
BACKGROUND: Team diversity is recognized not only as an equity issue but also a catalyst for improved performance through diversity in knowledge and practices. However, team diversity data in healthcare are limited and it is not known whether it may affect outcomes in surgery. This study examined the association between anaesthesia-surgery team sex diversity and postoperative outcomes. METHODS: This was a population-based retrospective cohort study of adults undergoing major inpatient procedures between 2009 and 2019. The exposure was the hospital percentage of female anaesthetists and surgeons in the year of surgery. The outcome was 90-day major morbidity. Restricted cubic splines were used to identify a clinically meaningful dichotomization of team sex diversity, with over 35% female anaesthetists and surgeons representing higher diversity. The association with outcomes was examined using multivariable logistic regression. RESULTS: Of 709 899 index operations performed at 88 hospitals, 90-day major morbidity occurred in 14.4%. The median proportion of female anaesthetists and surgeons was 28 (interquartile range 25-31)% per hospital per year. Care in hospitals with higher sex diversity (over 35% female) was associated with reduced odds of 90-day major morbidity (OR 0.97, 95% c.i. 0.95 to 0.99; P = 0.02) after adjustment. The magnitude of this association was greater for patients treated by female anaesthetists (OR 0.92, 0.88 to 0.97; P = 0.002) and female surgeons (OR 0.83, 0.76 to 0.90; P < 0.001). CONCLUSION: Care in hospitals with greater anaesthesia-surgery team sex diversity was associated with better postoperative outcomes. Care in a hospital reaching a critical mass with over 35% female anaesthetists and surgeons, representing higher team sex-diversity, was associated with a 3% lower odds of 90-day major morbidity.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".