Secondary mitral regurgitation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Secondary mitral regurgitation commonly complicates heart failure. Although the evidence for its management is most robust for treating the underlying cardiomyopathy, treatment aimed at additionally reducing the severity of mitral regurgitation with a percutaneous edge-to-edge device, MitraClip, has recently emerged. RECENT FINDINGS: Despite the use of contemporary evidence-based heart failure therapies, patients with secondary mitral regurgitation and heart failure continue to remain at high risk for adverse clinical events; in both the MITRA-FR and COAPT trials, an extremely high event rate was evident in the medically managed arms over the respective 12-24-month follow-up. Data supporting the use of MitraClip to mitigate adverse outcomes in secondary mitral regurgitation is, however, conflicting. In MITRA-FR no difference was noted between MitraClip compared with the medically managed arm for the composite of all-cause death or heart failure hospitalization at 12 months. However, in COAPT, a significant reduction in the rate of heart failure re-hospitalization over 2 years was evident with MitraClip compared with medical therapy alone. SUMMARY: Recommendations exist for the use of MitraClip in patients with primary mitral regurgitation and prohibitive surgical risk. However, with the divergent results of two recent high-quality randomized trials, its role in patients with secondary mitral regurgitation remains controversial.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".