Stage‐based approach to predict left ventricular reverse remodeling after mitral repair
Bibliographic record
Abstract
BACKGROUND: Although predictors of reverse left ventricular (LV) remodeling postmitral valve repair are critical for guiding perioperative decision-making, there remains a paucity of randomized, prospective data to support the criteria that potential predictor variables must meet. METHODS AND RESULTS: The CAMRA CardioLink-2 randomized trial allocated 104 patients to either leaflet resection or preservation strategies for mitral repair. The correlation of indexed left ventricular end-systolic volume (LVESVI), indexed left ventricular end-diastolic volume (LVEDVI), and left ventricular ejection fraction (LVEF) were tested with univariate analysis and subsequently with multivariate analysis to determine independent predictors of reverse remodeling at discharge and at 12 months postoperatively. At discharge, both LVESVI and LVEDVI were independently associated with their preoperative values (p < .001 for both) and LVEF by preoperative LVESVI (p < .001). Mitral ring size was favorably associated with the change in LVESVI (p < .05) and LVEF (p < .01) from predischarge to 12 months, while the mean mitral valve gradient after repair was adversely associated with the change in LVESVI (p < .05) and LVEDVI (p < .05). No significant associations were found between reverse remodeling and coaptation height nor mitral repair technique. CONCLUSIONS: Beyond confirming the lack of impact of mitral repair technique on reverse remodeling, this investigation suggests that recommending surgery before significant LV dilatation or dysfunction, as well as higher postoperative mitral valve hemodynamic performance, may enhance remodeling capacity following mitral repair.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".