Abstract 11277: Impact of Sex-Specific Echocardiographic Evaluation on Outcome of Patients With Organic Mitral Regurgitation
Bibliographic record
Abstract
Background: In patients with organic mitral regurgitation (OMR), women are less often diagnosed with severe disease and less referred to intervention. One of the potential explanations is the use of non-indexed and non-sex-specific guideline’s-based triggers for surgery. Aims: To compare predictors of the composite of cardio-vascular hospitalization and mortality under medical management in men and women with OMR. Methods: Between January and December 2014, 295 women and 269 men were diagnosed with at least mild isolated OMR. Event-free survival was compared with the use of adjusted Cox regression and inverse probability treatment weighting. Best thresholds predicting the composite endpoint at 3 years were derives from the Youden index on ROC curves analyses in both sexes separately. Results: Mean age was 68.5 ±14.1 years and the majority of patients (46%) had moderate OMR. Severity of OMR was similar between groups and inverse propensity weighting was used to balance clinical characteristics between sexes. Despite unadjusted and adjusted similar even-free survival under medical management, left atrium (LA) and left ventricle (LV) cavities were larger in men than in women (all p<0.001). Interestingly, even after indexation by BSA, thresholds associated with higher mortality were different between men and women (LV mass index: 118 g/m 2 in men, 88g/m 2 in women; LV ejection fraction: ≤ 55% in men, ≤ 65% in women; systolic pulmonary artery pressure: >42mmHg in men, >29mmHg in women), except for LA volume index (>32ml/m 2 in both sexes) . The use of sex-specific thresholds improves the prediction of events (p=0.002). Conclusion: Management of OMR should integrate sex-specific cut-points to trigger intervention, even for echo parameters indexed to BSA in order to harmonize outcomes between men and women with OMR.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".