<i>Effect of Gender on Postoperative Morbidity and Mortality Outcomes: A Retrospective Cohort Study</i>
Why this work is in the frame
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Bibliographic record
Abstract
Full extent of gender differences on postoperative outcomes has never been studied on large scale, specifically postoperative complications. This study aims to assess the effect of gender on 30-day morbidity and mortality after major surgery. A retrospective cohort study was carried out using data of patients undergoing major surgeries from the American College of Surgeons' National Surgical Quality Improvement Program database between 2008 and 2011. Demographics, pre- and perioperative risk factors, as well as 30-day morbidities, both overall and specific, were reviewed. The 30-day mortality data were also assessed. Multivariate logistic regression analyses, basic (Adj1) and extended (Adj2), were used to assess the association between gender and outcomes. Out of 1,409,131 patients, 57.2 per cent were females. Females had lower prevalence of most system-specific risk variables. Overall morbidities were also lower in females versus males, even after adjustment for variables [total overall morbidity: ORadj2 = 0.9 (0.89-0.92), P < 0.0001] except in some cases such as after cardiac surgeries [ORadj2 = 1.29 (1.14-1.44), P < 0.0001] and vascular surgeries [ORadj2 = 1.14 (1.10-1.18), P < 0.0001], where overall morbidities of females were higher. Specific morbidities were also lower in females than in males in all types of complications except central nervous system-related postoperative complications [ORadj2 = 1.15 (1.08-1.22), P < 0.0001] and return to the operating room [ORadj2 = 1.06 (1.04-1.08), P < 0.0001]. The 30-day mortality rate for females was lower than males [ORadj2 = 0.99 (0.96-1.03), P = 0.94]. Female gender was associated with less perioperative morbidity and mortality versus males, but they did worse after cardiovascular procedures and had more central nervous system-related complications. These outcomes should be taken into consideration by surgeons and should be evaluated further in future studies.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it