Surgeon Gender and Early Complications in Elective Surgery
Bibliographic record
Abstract
OBJECTIVE: To examine the association between surgeon gender and early postoperative complications, including 30-day death and readmission, in elective surgery. BACKGROUND: Variations between male and female surgeon practice patterns may be a source of bias and gender inequality in the surgical field, perhaps impacting the quality of care. However, there are limited and conflicting studies regarding the association between surgeon gender and postoperative outcomes. METHODS: MEDLINE and Embase were searched in October 2023 for observational studies, including patients who underwent elective surgery requiring general or regional anesthesia across multiple surgical specialties. Multiple independent blinded reviewers oversaw the data selection, extraction, and quality assessment according to the PRISMA, MOOSE, and Newcastle Ottawa Scale guidelines. Data were pooled as odds ratios, using a generic inverse-variance random-effects model. RESULTS: Of 944 abstracts screened, 11 studies were included in this systematic review and meta-analysis. A total of 4,440,740 postoperative patients were assessed for a composite primary outcome of mortality, readmission, and other complications within 30 days of elective surgery, with a total of 325,712 (7.3%) surgeries performed by 7072 (10.9%) female surgeons. There was no association between surgeon gender and the composite of mortality, readmission, and/or complications (odds ratio=0.97, 95% CI 0.95-1.00; I2 =64.9%; P =0.001). CONCLUSIONS: These results support that surgeon gender is not associated with early postoperative outcomes, including mortality, readmission, or other complications in elective surgery. These findings encourage patients, health care providers, and stakeholders not to consider surgeon gender as a risk factor for postoperative complications.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".