Predictors of Disease Progression in Pediatric Dilated Cardiomyopathy
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Despite medical advances, children with dilated cardiomyopathy (DCM) remain at high risk of death or need for cardiac transplantation. We sought to identify predictors of disease progression in pediatric DCM. METHODS AND RESULTS: The Pediatric Heart Network evaluated chronic DCM patients with prospective echocardiographic and clinical data collection during an 18-month follow-up. Inclusion criteria were age <22 years and DCM disease duration >2 months. Patients requiring intravenous inotropic/mechanical support or listed status 1A/1B for transplant were excluded. Disease progression was defined as an increase in transplant listing status, hospitalization for heart failure, intravenous inotropes, mechanical support, or death. Predictors of disease progression were identified using Cox proportional hazards modeling and classification and regression tree analysis. Of the 127 patients, 28 (22%) had disease progression during the 18-month follow-up. Multivariable analysis identified older age at diagnosis (hazard ratio=1.14 per year; P<0.001), larger left ventricular (LV) end-diastolic M-mode dimension z-score (hazard ratio=1.49; P<0.001), and lower septal peak systolic tissue Doppler velocity z-score (hazard ratio=0.81; P=0.01) as independent predictors of disease progression. Classification and regression tree analysis stratified patients at risk of disease progression with 89% sensitivity and 94% specificity based on LV end-diastolic M-mode dimension z-score ≥7.7, LV ejection fraction <39%, LV inflow propagation velocity (color M-mode) z-score <-0.28, and age at diagnosis ≥8.5 months. CONCLUSIONS: In children with chronic stable DCM, a combination of diagnosis after late infancy and echocardiographic parameters of larger LV size and systolic and diastolic function predicted disease progression. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00123071.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it