Association Between Surgeon and Anesthesiologist Sex Discordance and Postoperative Outcomes
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to examine the effect of surgeon-anesthesiologist sex discordance on postoperative outcomes. SUMMARY BACKGROUND DATA: Optimal surgical outcomes depend on teamwork, with surgeons and anesthesiologists forming two key components. There are sex and sex-based differences in interpersonal communication and medical practice which may contribute to patients' perioperative outcomes. METHODS: We performed a population-based, retrospective cohort study among adult patients undergoing 1 of 25 common elective or emergent surgical procedures from 2007 to 2019 in Ontario, Canada. We assessed the association between differences in sex between surgeon and anesthesiologists (sex discordance) on the primary endpoint of adverse postoperative outcome, defined as death, readmission, or complication within 30 days following surgery using generalized estimating equations. RESULTS: Among 1,165,711 patients treated by 3006 surgeons and 1477 anesthesiologists, 791,819 patients were treated by sex concordant teams (male surgeon/male anesthesiologist: 747,327 and female surgeon/female anesthesiologist: 44,492), whereas 373,892 were sex discordant (male surgeon/female anesthesiologist: 267,330 and female surgeon/male anesthesiologist: 106,562). Overall, 12.3% of patients experienced >1 adverse postoperative outcomes of whom 1.3% died. Sex discordance between surgeon and anesthesiologist was not associated with a significant increased likelihood of composite adverse postoperative outcomes (adjusted odds ratio 1.00, 95% confidence interval 0.97-1.03). CONCLUSIONS: We did not demonstrate an association between intraoperative surgeon and anesthesiologist sex discordance on adverse postoperative outcomes in a large patient cohort. Patients, clinicians, and administrators may be reassured that physician sex discordance in operating room teams is unlikely to clinically meaningfully affect patient outcomes after surgery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".