Association between patient-surgeon gender concordance and mortality after surgery in the United States: retrospective observational study
Bibliographic record
Abstract
OBJECTIVE: To determine whether patient-surgeon gender concordance is associated with mortality of patients after surgery in the United States. DESIGN: Retrospective observational study. SETTING: Acute care hospitals in the US. PARTICIPANTS: 100% of Medicare fee-for-service beneficiaries aged 65-99 years who had one of 14 major elective or non-elective (emergent or urgent) surgeries in 2016-19. MAIN OUTCOME MEASURES: Mortality after surgery, defined as death within 30 days of the operation. Adjustments were made for patient and surgeon characteristics and hospital fixed effects (effectively comparing patients within the same hospital). RESULTS: Among 2 902 756 patients who had surgery, 1 287 845 (44.4%) had operations done by surgeons of the same gender (1 201 712 (41.4%) male patient and male surgeon, 86 133 (3.0%) female patient and female surgeon) and 1 614 911 (55.6%) were by surgeons of different gender (52 944 (1.8%) male patient and female surgeon, 1 561 967 (53.8%) female patient and male surgeon). Adjusted 30 day mortality after surgery was 2.0% for male patient-male surgeon dyads, 1.7% for male patient-female surgeon dyads, 1.5% for female patient-male surgeon dyads, and 1.3% for female patient-female surgeon dyads. Patient-surgeon gender concordance was associated with a slightly lower mortality for female patients (adjusted risk difference -0.2 percentage point (95% confidence interval -0.3 to -0.1); P<0.001), but a higher mortality for male patients (0.3 (0.2 to 0.5); P<0.001) for elective procedures, although the difference was small and not clinically meaningful. No evidence suggests that operative mortality differed by patient-surgeon gender concordance for non-elective procedures. CONCLUSIONS: Post-operative mortality rates were similar (ie, the difference was small and not clinically meaningful) among the four types of patient-surgeon gender dyads.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".