Respect Versus Resect Approaches for Mitral Valve Repair: A Meta-Analysis of Reconstructed Time-to-Event Data
Bibliographic record
Abstract
Mitral valve repair (MVr) has been associated with superior long-term survival and freedom from valve-related complications compared with mitral valve replacement for primary mitral regurgitation (MR). The 2 main approaches for MVr are chordal replacement ("respect approach") and leaflet resection ("resect approach"). We performed a systematic review and a meta-analysis using 3 search databases to compare the long-term end points between both approaches. The primary end point was long-term survival. The secondary end points were long-term MR recurrence and reoperation. After reconstruction of time-to-event data for the individual survival analysis, pooled Kaplan-Meier curves for the end points were generated. A total of 14 studies (5,565 patients) were included in the analysis. The respect approach was associated with superior survival compared with the resect approach in the overall sample (hazard ratio [HR] 0.73, 95% confidence interval [CI] 0.56 to 0.96, p = 0.024, n = 3,901 patients) but not in the risk-adjusted sample (HR 1.00, 95% CI 0.55 to 1.82, p = 0.991, n = 620 patients). There was no difference between the approaches in the rate of MR recurrence in the overall sample (HR 1.39, 95% CI 0.92 to 2.08, p = 0.116, n = 1,882 patients) or in the risk-adjusted sample (HR 1.62, 95% CI 0.76 to 3.47, p = 0.211, n = 288 patients). The data for reoperation were only available in the overall sample and did not reveal a difference (HR 0.92, 95% CI 0.62 to 1.35, p = 0.663, n = 3,505 patients). In conclusion, the current evidence suggests no difference in long-term mortality, MR recurrence, or reoperation between the resect and respect approaches for MVr after adjusting for patient risk factors. More long-term follow-up data are warranted.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.031 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".