Evolving Perspectives in Surgery for Mitral Regurgitation: Why Sex Matters
Bibliographic record
Abstract
There is a growing body of evidence investigating sex differences in the presentation, assessment, and outcomes of patients with mitral regurgitation (MR) undergoing mitral valve surgery. It has been shown that women present at older ages, with more comorbidities and more severe symptoms. Compared with male patients, female patients have longer intervals to surgery, lower rates of surgery, and receive fewer mitral valve repairs (as opposed to replacements). On imaging, left ventricular cavity sizes and many quantitative measures of MR severity differ significantly by sex, and current guidelines do not account for this. While sex differences in surgical outcomes have been documented, these are largely limited to primary MR and are based on older studies, underscoring the need for further research. Data on sex differences in transcatheter interventions for MR are inconclusive and heterogeneous, complicating comparisons to surgery. To address these disparities, sex-specific thresholds for intervention in primary MR, standardization of the quantification of MR severity by sex, and further prospective studies are required. As we move into an era of precision medicine, it is critical to recognize sex as a key determinant of cardiovascular care. In patients undergoing surgery for MR, further research should evaluate whether current intervention thresholds and management pathways are appropriately tailored to female patients.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".