Population Study of Sex-Based Outcomes After Surgical Aortic Valve Replacement
Bibliographic record
Abstract
Background Surgical aortic valve replacement (SAVR) is a key strategy for the treatment of aortic valve disease. However, studies have involved primarily male patients, and whether the benefits of this approach can be extrapolated to female patients is unclear. Methods Clinical and administrative datasets for 12,207 patients undergoing isolated SAVR in Ontario from 2008 to 2019 were linked. Male and female patients were balanced using inverse probability treatment weighting. Mortality, endocarditis, and major hemorrhagic and thrombotic events, as well as 2 composite outcomes—major adverse cerebral and cardiovascular events (MACCE) and patient-derived adverse cardiovascular and noncardiovascular events (PACE)—and their component events, were compared in the weighted groups with a stratified log-rank test. Results A total of 7485 male patients and 4722 female patients were included in the study. Median follow-up was 5.2 years in both sexes. All-cause mortality did not differ between sexes (hazard ratio [HR] 0.949 [95% confidence interval {CI} 0.851-1.059]). Male sex was associated with an increased risk of new-onset dialysis (HR 0.689 [95% CI 0.488-0.974]). Female sex was associated with a significantly increased risk of both new-onset heart failure (HR 1.211 [95% CI 1.051-1.394], P = 0.0081) and heart failure hospitalization (HR 1.200 [95% CI 1.036-1.390], P = 0.015). No statistically significant differences were seen in any of the other secondary outcomes between sexes. Conclusions This population health study demonstrated that survival did not differ between male and female patients undergoing SAVR. Significant sex-related differences were found in the risk of heart failure and new-onset dialysis, but these findings should be considered exploratory and require further study.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".