Lessons learned from the Pediatric Cardiomyopathy Registry (PCMR) Study Group
Bibliographic record
Abstract
Cardiomyopathy is a rare disorder of the heart muscle, affecting 1.13 cases per 100,000 children, from birth to 18 years of age. Cardiomyopathy is the leading cause of heart transplantation in children over the age of 1. The Pediatric Cardiomyopathy Registry funded in 1994 by the National Heart, Lung, and Blood Institute was established to examine the epidemiology of the disease in children below 18 years of age. More than 3500 children across the United States and Canada have been enrolled in the Pediatric Cardiomyopathy Registry, which has followed-up these patients until death, heart transplantation, or loss to follow-up. The Pediatric Cardiomyopathy Registry has provided the most in-depth illustration of this disease regarding its aetiology, clinical course, associated risk factors, and patient outcomes. Data from the registry have helped in guiding the clinical management of cardiomyopathy in children under 18 years of age; however, questions still remain regarding the most clinically effective diagnostic and treatment approaches for these patients. Future directions of the registry include the use of next-generation whole-exome sequencing and cardiac biomarkers to identify aetiology-specific treatments and improve diagnostic strategies. This article provides a brief synopsis of the work carried out by the Pediatric Cardiomyopathy Registry since its inception, including the current knowledge on the aetiologies, outcomes, and treatments of cardiomyopathy in children.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".