Uptake of Predictive Genetic Testing and Cardiac Evaluation for Children at Risk for an Inherited Arrhythmia or Cardiomyopathy
Bibliographic record
Abstract
Predictive genetic testing in minors should be considered when clinical intervention is available. Children who carry a pathogenic variant for an inherited arrhythmia or cardiomyopathy require regular cardiac screening and may be prescribed medication and/or be told to modify their physical activity. Medical genetics and pediatric cardiology charts were reviewed to identify factors associated with uptake of genetic testing and cardiac evaluation for children at risk for long QT syndrome, hypertrophic cardiomyopathy or arrhythmogenic right ventricular cardiomyopathy. The data collected included genetic diagnosis, clinical symptoms in the carrier parent, number of children under 18 years of age, age of children, family history of sudden cardiac arrest/death, uptake of cardiac evaluation and if evaluated, phenotype for each child. We identified 97 at risk children from 58 families found to carry a pathogenic variant for one of these conditions. Sixty six percent of the families pursued genetic testing and 73% underwent cardiac screening when it was recommended. Declining predictive genetic testing was significantly associated with genetic specialist recommendation (p < 0.001) and having an asymptomatic carrier father (p = 0.006). Cardiac evaluation was significantly associated with uptake of genetic testing (p = 0.007). This study provides a greater understanding of factors associated with uptake of genetic testing and cardiac evaluation in children at risk of an inherited arrhythmia or cardiomyopathy. It also identifies a need to educate families about the importance of cardiac evaluation even in the absence of genetic testing.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".