Technical performance scores are predictors of midterm mortality and reinterventions following congenital mitral valve repair†
Bibliographic record
Abstract
OBJECTIVES: The Technical Performance Score (TPS) has been shown to be predictive of postoperative mortality, morbidities and reinterventions following various cardiac procedures in children. We hypothesized that TPS is also a predictor of mitral valve repair outcomes. METHODS: A review of patients who underwent mitral valve repair from January 2000 to December 2013 was performed. Primary repair of complete atrioventricular defect was excluded. The scores were determined according to previously published criteria based on the need for reintervention and predischarge echocardiograms: Class 1 (no residua), Class 2 (minor residua) or Class 3 (pacemaker implantation, major residua or reintervention for major residua prior to discharge). Cox proportional hazard models and Kaplan-Meier estimator were used. RESULTS: A total of 587 patients underwent mitral repair (median age 2.6 years). Median follow-up duration was 3 years. There were 125 (21.3%) post-discharge mitral reinterventions and freedom from reintervention was 85.2%, 78.2% and 69.4% at 1, 2 and 5 years, respectively. Both TPS Class 2 [hazard ratio (HR) 3.6, 95% confidence interval (CI) 1.4-10.0; P = 0.02] and Class 3 (HR 8.7, 95% CI 3.0-25.1; P < 0.001) were associated with post-discharge reinterventions. There were 31 late deaths/transplantations, and transplant-free survival at 1, 2 and 5 years was 97.8%, 95.3% and 93.2%. TPS 3 was associated with decreased post-discharge transplant-free survival (HR 5.5, 95% CI 1.2-25.0; P = 0.03). Post-discharge mitral reintervention was not associated with increased mortality. CONCLUSIONS: The TPS is a strong predictor of midterm mortality and post-discharge mitral reintervention in congenital patients who underwent 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".