Outcome of Mitral Valve Repair or Replacement: A Comparison by Propensity Score Analysis
Bibliographic record
Abstract
BACKGROUND: There are no randomized trials comparing outcomes after mitral valve (MV) repair and replacement. Propensity scoring is a powerful tool that has the potential to reduce selection bias in nonrandomized studies. METHODS: From the BC Cardiac Registries, 2,060 patients presented for MV surgery, with or without CABG between 1991 and 2000. We then identified 322 MV repairs who were then matched by propensity score to an equal number of MV replacement patients. We compared survival and freedom from re-operation outcomes using Cox proportional hazards model analysis. Multivariable analysis was then used to compare outcomes in 358 MV repair patients with 352 MV replacement patients who had undergone chordal sparing surgery. RESULTS: The comparison groups generated using propensity scores were well balanced with respect to all collected baseline risk factors. Median follow-up time was 3.4 years. Patients undergoing MV repair had significantly improved survival (RR 0.46; 95% CI, 0.28 to 0.75) but a trend toward more re-operations (RR 2.11; 95% CI, 1.00 to 4.47) compared with patients undergoing replacement. Mitral valve repair patients still had better survival (RR 0.52; 95% CI, 0.32 to 0.85) compared with MV replacement patients who had undergone chordal sparing surgery. CONCLUSIONS: We used propensity score methods to reduce selection bias in a population-based cohort of patients undergoing MV repair/replacement. Repair was associated with better survival, but a trend to increased re-operation.
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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 | 0.030 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".