Association Between Surgeon/Anesthesiologist Sex Discordance and 1-year Mortality Among Adults Undergoing Noncardiac Surgery
Bibliographic record
Abstract
OBJECTIVE: To investigate the association between surgeon-anesthesiologist sex discordance and patient mortality after noncardiac surgery. BACKGROUND: Evidence suggests different practice patterns exist among female and male physicians. However, the influence of physician sex on team-based practices in the operating room and subsequent patient outcomes remains unclear in the context of noncardiac surgery. METHODS: We conducted a population-based, retrospective cohort study of adult Ontario residents who underwent index, inpatient noncardiac surgery between January 2007 and December 2017. The primary exposure was physician sex discordance (ie, the surgeon and anesthesiologist were of the opposite sex). The primary outcome was 1-year mortality. The association between physician sex discordance and patient outcomes was modeled using multivariable Cox proportional hazard regression with adjustment for relevant physician, patient, and hospital characteristics. RESULTS: Of 541,209 patients, 158,084 (29.2%) were treated by sex-discordant physician teams. Physician sex discordance was associated with a lower rate of mortality at 1 year [5.2% vs. 5.7%; adjusted HR: 0.95 (0.91-0.99)]. Patients treated by teams composed of female surgeons and male anesthesiologists were more likely to be alive at 1 year than those treated by all-male physician teams [adjusted HR: 0.90 (0.81-0.99)]. CONCLUSIONS: Noncardiac surgery patients had a lower likelihood of 1-year mortality when treated by sex-discordant surgeon-anesthesiologist teams. The likelihood of mortality was further reduced if the surgeon was female. Further research is needed to explore the underlying mechanisms of these observations and design strategies to diversify operating room teams to optimize performance and patient outcomes.
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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 | 0.005 |
| 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) | 0.001 | 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".