Outcome Prediction After Tetralogy of Fallot Repair: A Prospective Clinical and Cardiovascular Magnetic Resonance Study
Bibliographic record
Abstract
BACKGROUND: Identification of individuals at risk for major adverse cardiovascular events is essential for contemporary management of patients with repaired tetralogy of Fallot. We sought to identify clinical and cardiovascular magnetic resonance imaging (CMR) predictors of adverse clinical outcomes in repaired tetralogy of Fallot. METHODS: Children and adults prospectively enrolled in the CORRELATE (Comprehensive Outcomes Registry Late After Tetralogy of Fallot Repair) registry followed in North American, European, and Asian centers were studied. All patients had at least moderate pulmonary regurgitation and CMR at enrollment. Time-to-event analyses were performed from CMR completion to primary outcome, defined as mortality, resuscitated sudden death, sustained ventricular arrhythmia, or heart failure admission. Principal component analysis was used to create distinct CMR scores that collectively captured 80% of the variance among 10 CMR measures (systolic function, biventricular volumes/mass, and biatrial areas). RESULTS: In 720 patients (55% male, median age 30.3±14 years, 78% adult) with mean follow-up 5.7±1.8 years, the primary outcome occurred in 38 patients (5.2%) at a rate of 0.9/100 patient-years. A well-calibrated risk scoring system was created for prediction of the primary outcome at 5 years based on 5 predictors: age, diabetes, right ventricular systolic pressure, and 2 CMR principal component scores (predominantly reflecting atrial areas in the first principal component score and ventricular volumes in the second principal component score) (c-statistic for the composite risk score 0.79 [95% Cl, 0.71-0.88]). CONCLUSIONS: Clinical and imaging characteristics can contribute to risk prediction in repaired tetralogy of Fallot. Further study will be required to evaluate the utility of a risk scoring system for identification of individuals who may benefit from enhanced surveillance, intensified medical therapy, and/or optimally timed intervention.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".