Association Between Anesthesiologist Sex and Patients’ Postoperative Outcomes: A Population-based Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To examine the association of anesthesiologist sex on postoperative outcomes. BACKGROUND: Differences in patient postoperative outcomes exist, depending on whether the primary surgeon is male or female, with better outcomes seen among patients treated by female surgeons. Whether the intraoperative anesthesiologist's sex is associated with differential postoperative patient outcomes is unknown. METHODS: We performed a population-based, retrospective cohort study among adult patients undergoing one of 25 common elective or emergent surgical procedures from 2007 to 2019 in Ontario, Canada. We assessed the association between the sex of the intraoperative anesthesiologist and the primary end point of the adverse postoperative outcome, defined as death, readmission, or complication within 30 days after surgery, using generalized estimating equations. RESULTS: Among 1,165,711 patients treated by 3006 surgeons and 1477 anesthesiologists, 311,822 (26.7%) received care from a female anesthesiologist and 853,889 (73.3%) from a male anesthesiologist. Overall, 10.8% of patients experienced one or more adverse postoperative outcomes, of whom 1.1% died. Multivariable adjusted rates of the composite primary end point were higher among patients treated by male anesthesiologists (10.6%) compared with female anesthesiologists (10.4%; adjusted odds ratio 1.02, 95% CI: 1.00-1.05, P =0.048). CONCLUSIONS: We demonstrated a significant association between sex of the intraoperative anesthesiologist and patient short-term outcomes after surgery in a large cohort study. This study supports the growing literature of improved patient outcomes among female practitioners. The underlying mechanisms of why outcomes differ between male and female physicians remain elusive and require further in-depth study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".