Abstract 9397: Predictors of Disease Progression in Pediatric Dilated Cardiomyopathy
Bibliographic record
Abstract
Objective: Identify predictors of disease progression in pediatric dilated cardiomyopathy (DCM). Methods: The Pediatric Heart Network Ventricular Volume Variability Study evaluated chronic DCM patients with serial prospective echocardiographic and clinical data collection over an 18 month follow-up. Inclusion criteria were age <22 years and DCM disease duration > 2 months with exclusion of those needing IV inotropic or mechanical support, and those listed status 1A/1B for transplant. Disease progression was defined as an increase in transplant listing status, hospitalization for heart failure, IV inotropes, mechanical support, or death during follow-up. Predictors of disease progression were identified using logistic regression and classification and regression tree (CART) analysis. Results: Of the 127 patients, 28 (22%) met criteria for disease progression during the 18 month follow-up period. Multivariable analysis (c-statistic=0.90) identified older age at diagnosis (OR=1.16 per yr, p=0.003), larger left ventricular (LV) end-diastolic m-mode dimension z-score (LVEDDz) (OR 1.77, p<0.001) and lower septal peak systolic tissue Doppler velocity z-score (OR=0.68, p=0.04) as independent predictors of disease progression. CART analysis risk-stratified patients for significant disease progression with 89% sensitivity and 94% specificity based on LVEDDz ≥7.7, LV ejection fraction <38.2%, LV inflow propagation velocity (color m-mode) z-score < -0.28, and age at diagnosis ≥ 8.5 months. (Figure 1) Conclusion: In pediatric patients with DCM, diagnosis after late infancy and echocardiographic parameters of LV size, systolic and diastolic function were independently associated with disease progression, and may be used to reliably risk stratify DCM patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| 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, 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".