Percutaneous Repair or Medical Treatment for Secondary Mitral Regurgitation: Outcomes at 2 Years
Bibliographic record
Abstract
AIMS: The MITRA-FR trial showed that among symptomatic patients with severe secondary mitral regurgitation, percutaneous repair did not reduce the risk of death or hospitalization for heart failure at 12 months compared with guideline-directed medical treatment alone. We report the 24-month outcome from this trial. METHODS AND RESULTS: or regurgitant volume >30 mL), and left ventricular ejection fraction between 15% and 40% to undergo percutaneous valve repair plus medical treatment (intervention group, n = 152) or medical treatment alone (control group, n = 152). The primary efficacy outcome was the composite of all-cause death and unplanned hospitalization for heart failure at 12 months. At 24 months, all-cause death and unplanned hospitalization for heart failure occurred in 63.8% of patients (97/152) in the intervention group and 67.1% (102/152) in the control group [hazard ratio (HR) 1.01, 95% confidence interval (CI) 0.77-1.34]. All-cause mortality occurred in 34.9% of patients (53/152) in the intervention group and 34.2% (52/152) in the control group (HR 1.02, 95% CI 0.70-1.50). Unplanned hospitalization for heart failure occurred in 55.9% of patients (85/152) in the intervention group and 61.8% (94/152) in the control group (HR 0.97, 95% CI 0.72-1.30). CONCLUSIONS: In patients with severe secondary mitral regurgitation, percutaneous repair added to medical treatment did not significantly reduce the risk of death or hospitalization for heart failure at 2 years compared with medical treatment alone.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".