Cardiomyopathy in Children: Classification and Diagnosis: A Scientific Statement From the American Heart Association
Bibliographic record
Abstract
In this scientific statement from the American Heart Association, experts in the field of cardiomyopathy (heart muscle disease) in children address 2 issues: the most current understanding of the causes of cardiomyopathy in children and the optimal approaches to diagnosis cardiomyopathy in children. Cardiomyopathies result in some of the worst pediatric cardiology outcomes; nearly 40% of children who present with symptomatic cardiomyopathy undergo a heart transplantation or die within the first 2 years after diagnosis. The percentage of children with cardiomyopathy who underwent a heart transplantation has not declined over the past 10 years, and cardiomyopathy remains the leading cause of transplantation for children >1 year of age. Studies from the National Heart, Lung, and Blood Institute-funded Pediatric Cardiomyopathy Registry have shown that causes are established in very few children with cardiomyopathy, yet genetic causes are likely to be present in most. The incidence of pediatric cardiomyopathy is ≈1 per 100 000 children. This is comparable to the incidence of such childhood cancers as lymphoma, Wilms tumor, and neuroblastoma. However, the published research and scientific conferences focused on pediatric cardiomyopathy are sparcer than for those cancers. The aim of the statement is to focus on the diagnosis and classification of cardiomyopathy. We anticipate that this report will help shape the future research priorities in this set of diseases to achieve earlier diagnosis, improved clinical outcomes, and better quality of life for these children and their families.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".