Bibliographic record
Abstract
<div> It is uncertain whether sex is an independent risk factor for poor outcomes after non-cardiac surgery. We examined sex differences in short- and long-term mortality and morbidity in patients undergoing non-cardiac surgery in Alberta, Canada. Using linked administrative databases, we identified patients undergoing one of 45 different non-cardiac surgeries who were hospitalized between 2008 and 2019. Adjusted odds ratios (95% CI) were reported for mortality at 30-days, 6-months, and 1-year stratified by sex. Secondary outcomes including all-cause hospitalization, hospitalization for heart failure (HF), hospitalization for acute coronary syndrome (ACS), hospitalization for infection, hospitalization for stroke, and hospitalization for bleeding were also analyzed. Multivariate logistic regression was adjusted for age, sex, surgery type, the components of the Charlson Comorbidity Index, and the Revised Cardiac Risk Index. We identified 552,224 unique patients who underwent non-cardiac surgery of which 304,408 (55.1%) were female. Male sex was a predictor of mortality at 30-days (aOR 1.25 (1.14, 1.38), p<0.0001), 6-months (aOR 1.26 (1.20, 1.33), p<0.0001), and 1-year (aOR 1.25 (1.20, 1.31), p<0.0001). Similarly, male sex was a predictor of hospital readmission at 30-days (1.12 (1.09, 1.14), p<0.0001), 6-months (aOR 1.11 (1.10, 1.13), p<0.0001), and 1-year (aOR 1.06 (1.04, 1.07), p<0.0001). When the results were stratified by age, the effect of male sex on clinical outcome diminished for age ≥ 65years compared to younger patients. In conclusion, male patients undergoing non-cardiac surgery have higher risks of all-cause mortality and readmission after adjustment for baseline risk factor differences, particularly in those under 65-years-old. The overall incidence of readmission for stroke, bleeding, HF and ACS after non-cardiac surgery was low. The impact of male sex on clinical outcomes decreases with increasing age, suggesting the importance of considering the effect of both sex and age on clinical outcomes after non-cardiac surgery. </div>
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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) | 0.155 | 0.085 |
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; both teacher heads agree on what is shown here.
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".