Association Between Surgeon Sex and Days Alive at Home Following Surgery: A Population-Based Cohort Study
Bibliographic record
Abstract
Objective: The objective of this study was to measure potential associations between surgeon sex and number of days alive and at home (DAH). Background: Patients treated by female surgeons appear to have lower rates of mortality, complications, readmissions, and healthcare costs when compared with male surgeons. DAH is a validated measure, shown to better capture the patient experience of postoperative recovery. Methods: We conducted a retrospective study of adults (≥18 years of age) undergoing common surgeries between January 01, 2007 and December 31, 2019 in Ontario, Canada. The outcome measures were the number of DAH within 30-, 90-, and 365-days. The data was summarized using descriptive statistics and adjusted using multivariable generalized estimating equations. Results: During the study period, 1,165,711 individuals were included, of which 61.9% (N = 721,575) were female. Those managed by a female surgeon experienced a higher mean number of DAH when compared with male surgeons at 365 days (351.7 vs. 342.1 days; P < 0.001) and at each earlier time point. This remained consistent following adjustment for covariates, with patients of female surgeons experiencing a higher number of DAH at all time points, including at 365 days (343.2 [339.5–347.1] vs. 339.4 [335.9–343.0] days). Multivariable regression modeling revealed that patients of male surgeons had a significantly lower number of DAH versus female surgeons. Conclusions: Patients of female surgeons experienced a higher number of DAH when compared with those treated by male surgeons at all time points. More time spent at home after surgery may in turn lower costs of care, resource utilization, and potentially improve quality of life. Further studies are needed to examine these findings across other care contexts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".