Update on surgical repair in functional mitral valve regurgitation.
Bibliographic record
Abstract
Background: Functional mitral regurgitation (FMR) is common in patients with myocardial infarction or dilated cardiomyopathies, and portends a poor prognosis despite guideline-directed medical therapy. Surgical or transcatheter mitral repair for FMR from recent randomized clinical trials showed disappointing or conflicting results. Aims: To provide an update on the role of surgical repair in the management of FMR. Materials & Methods: A literature search was conducted utilizing PubMed, Ovid, Web of Science, Embase and Cochrane Library. The search terms included secondary/functional mitral regurgitation, ischemic mitral regurgitation, mitral repair, mitral replacement, mitral annuloplasty, transcatheter mitral repair, and percutaneous mitral repair. Randomized clinical trials over the past decade were the particular focus of this current review. Results: Recent data underlined the complexity and poor prognosis of FMR. Guideline-directed medical therapy and cardiac resynchronization, when indicated, should always be applied. Accurate assessment of the interplay between ventricular geometry and mitral valve function is essential to differentiate proportionate FMR from the disproportionate subgroup, which could be helpful in selecting appropriate transcatheter intervention strategies. Surgical repair, most commonly performed with an undersized ring annuloplasty, remains controversial. Adjunctive valvular or subvalvular repair techniques are evolving and may produce improved results in selected FMR patients. Conclusion: FMR resulted from complex valve-ventricular interaction and remodeling. Distinguishing proportionate FMR from disproportionate FMR is important in exploring their underlying mechanisms and to guide medical treatment with surgical or transcatheter interventions. Further studies are warranted to confirm the clinical benefit of appropriate surgical repair in selected FMR 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".