Overcoming the Disparity in Mitral Valve Repair: A Sex-Based Analysis of Long-Term Outcomes
Bibliographic record
Abstract
BACKGROUND: Sex disparities remain pervasive across most cardiovascular diseases and continue to demonstrate notably worse early and late outcomes for women, especially after surgical repair. This study aims to investigate outcomes of mitral valve (MV) repair by sex and identify opportunities for improvement. METHODS: A single center retrospective analysis of consecutive patients undergoing MV repair from May 2008 to February 2023 was conducted. In-hospital and long-term outcomes, including survival and symptomatic disease recurrence were examined by sex. Adjusted outcome analysis was performed using inverse-probability treatment weighting. RESULTS: In total, 490 patients underwent MV repair (median age, 65 years; interquartile range [IQR], 57-73 years; sternotomy n = 128 [26%], minimally invasive n = 362 [74%]), including 343 male and 147 female patients. Median follow-up time was 5.4 years (IQR, 3.1-8.4 years). inverse-probability treatment weighting-adjusted 30-day outcomes for female vs male, including death (1.4% vs 0.6%, P = .59) and major adverse cardiovascular events (8.2% vs 7.6%, P = .81), were not significantly different. Survival for female vs male after mitral valve repair was 94.9% vs 98.0% at 2 years, 91.4% vs 97.8% at 4 years, and 87.2% vs 88.7% at 8 years (hazard ratio, 0.52; IQR, 0.19-1.44). Both unadjusted and inverse-probability treatment weighting-adjusted Cox-regression hazard ratios for survival and freedom from symptomatic disease recurrence demonstrated no significant difference between sexes at long-term follow-up. CONCLUSIONS: These contemporary results are encouraging and suggest that a critical "bridging of the gap" between sexes is possible with comprehensive efforts including earlier detection and awareness and improved surgical techniques, though other factors may be important to explore further.
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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 | 0.002 | 0.005 |
| 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) | 0.001 | 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".