Sex-related differences among patients undergoing surgical aortic valve replacement—a propensity score matched study
Bibliographic record
Abstract
OBJECTIVES: We investigated the sex-related difference in characteristics and 2-year outcomes after surgical aortic valve replacement (SAVR) by propensity-score matching (PSM). METHODS: Data from 2 prospective registries, the INSPIRIS RESILIA Durability Registry (INDURE) and IMPACT, were merged, resulting in a total of 933 patients: 735 males and 253 females undergoing first-time SAVR. The PSM was performed to assess the impact of sex on the SAVR outcomes, yielding 433 males and 243 females with comparable baseline characteristics. RESULTS: Females had a lower body mass index (median 27.1 vs 28.0 kg/m2; P = 0.008), fewer bicuspid valves (52% vs 59%; P = 0.036), higher EuroSCORE II (mean 2.3 vs 1.8%; P < 0.001) and Society of Thoracic Surgeons score (mean 1.6 vs 0.9%; P < 0.001), were more often in New York Heart Association functional class III/IV (47% vs 30%; P < 0.001) and angina Canadian Cardiovascular Society III/IV (8.2% vs 4.4%; P < 0.001), but had a lower rate of myocardial infarction (1.9% vs 5.2%; P = 0.028) compared to males. These differences vanished after PSM, except for the EuroSCORE II and Society of Thoracic Surgeons scores, which were still significantly higher in females. Furthermore, females required smaller valves (median diameter 23.0 vs 25.0 mm, P < 0.001). There were no differences in the length of hospital stay (median 8 days) or intensive care unit stay (median 24 vs 25 hours) between the 2 sexes. At 2 years, post-SAVR outcomes were comparable between males and females, even after PSM. CONCLUSIONS: Despite females presenting with a significantly higher surgical risk profile, 2-year outcomes following SAVR were comparable between males and females.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".