Rethinking Childhood-Onset Hypertrophic Cardiomyopathy: A Review of Molecular Mechanisms and Unique Therapy Considerations
Bibliographic record
Abstract
Childhood-onset hypertrophic cardiomyopathy (HCM) is a cardiac disorder presenting unique diagnostic and therapeutic challenges in children that require tailored clinical attention compared to HCM arising at other life stages. Despite this, current treatment strategies specific to childhood-onset HCM are lacking and are predominantly extrapolated from adult-specific treatment guidelines. This review explores the molecular basis, clinical implications, and management strategies specific to childhood-onset HCM. Advances in molecular genetics have elucidated diverse pathogenic pathways and genotype-phenotype correlations, revealing age-specific disease modifiers distinct from adult-onset forms. Current management includes pharmacologic, surgical, and device-based interventions, tailored to individualized needs. However, there is a lack of evidence for the efficacy and safety profiles of these treatments in children, meaning children may be receiving sub-optimal care. Emerging approaches, such as gene-targeted therapies and precision medicine frameworks, show promise, but require further investigation. Enhancing early diagnosis and personalized care is crucial for improving outcomes and reducing long-term disease burden in affected children. This review underscores the necessity for specific research to refine risk stratification and treatment paradigms for childhood-onset HCM.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".