A Systematic Review and Meta-Analysis of Outcomes Following Mitral Valve Surgery in Patients with Significant Functional Mitral Regurgitation and Left Ventricular Dysfunction.
Bibliographic record
Abstract
BACKGROUND AND AIM OF THE STUDY: The surgical correction of functional mitral regurgitation (MR) remains challenging and controversial. The study aim was to systematically review the outcomes of surgical mitral valve repair (MVRpr) and mitral valve replacement (MVR) in patients with significant functional MR and left ventricular (LV) dysfunction. METHODS: A meta-analysis was performed of published data acquired from patients with moderate to severe functional MR and LV ejection fraction (LVEF) <40% who underwent surgical MVRpr or MVR. The data were meta-analyzed across studies using Bayesian hierarchical models when feasible. RESULTS: The search yielded 36 observational studies. The pooled proportion of operative mortality following MVRpr was 5% (33 studies; 2,231 patients; 95% credible interval (CrI) 4-7%), while that following MVR was 10% (10 studies; 389 patients; 95% CrI 5-18%). For patients undergoing MVRpr, pooled proportions of postoperative cerebrovascular accidents and renal failure were 2% (11 studies; 750 patients; 95% CrI 1-3%) and 9% (11 studies; 756 patients; 95% CrI 5-16%), respectively. The five-year actuarial survival rates following MVRpr across 12 studies ranged from 47% to 78% (median 66%). CONCLUSIONS: In selected patients with significant functional MR and LV dysfunction, surgical MVRpr and MVR can be performed with acceptable intermediate operative mortality risks.
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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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".