Sex Differences in Long‐Term Survival After Major Cardiac Surgery: A Population‐Based Cohort Study
Bibliographic record
Abstract
Background Little attention has been paid to the importance of sex in the long-term prognosis of patients undergoing cardiac surgery. Methods and Results We conducted a retrospective cohort study of Ontario residents, aged ≥40 years, who underwent coronary artery bypass grafting (CABG) and/or aortic, mitral, or tricuspid valve surgery between October 1, 2008, and December 31, 2016. The primary outcome was all-cause mortality. The mortality rate in each surgical group was calculated using the Kaplan-Meier method. The risk of death was assessed using multivariable Cox proportional hazard models. Sex-specific mortality risk factors were identified using multiplicative interaction terms. A total of 72 824 patients were included in the study (25% women). The median follow-up period was 5 (interquartile range, 3-7) years. The long-term age-standardized mortality rate was lowest in patients who underwent isolated CABG and highest among those who underwent combined CABG/multiple valve surgery. Women had significantly higher age-standardized mortality rate than men after CABG and combined CABG/mitral valve surgery. Men had lower rates of long-term mortality than women after isolated mitral valve repair, whereas women had lower rates of long-term mortality than men after isolated mitral valve replacement. We observed a statistically significant association between female sex and long-term mortality after adjustment for key risk factors. Conclusions Female sex was associated with long-term mortality after cardiac surgery. Perioperative optimization and long-term follow-up should be tailored to younger women with a history of myocardial infarction and percutaneous coronary intervention and older men with a history of chronic obstructive pulmonary disease and depression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".