Practice Variation among an International Group of Genetic Counselors on when to Offer Predictive Genetic Testing to Children at Risk of an Inherited Arrhythmia or Cardiomyopathy
Bibliographic record
Abstract
Cascade predictive genetic testing is available for many families as a means to identify individuals at risk of long QT syndrome (LQTS), catecholaminergic polymorphic ventricular tachycardia (CPVT), hypertrophic cardiomyopathy (HCM), and arrhythmogenic right ventricular cardiomyopathy (ARVC). The general issue of offering predictive genetic testing to minors has been an area of ethical debate among genetic counselors and other healthcare professionals for many years. An online questionnaire was circulated to four international genetic counseling associations to assess the views of cardiac genetic counselors regarding when to offer predictive genetic testing to children at risk of LQTS, CPVT, HCM, and ARVC. Analysis was both quantitative and qualitative. The study sample comprised 98 respondents. The majority reported that they offer predictive genetic testing before 5 years of age to children at risk of LQTS (83%) and CVPT (75%) and before 10 years of age to children at risk of HCM (66%) or ARVC (70%). Influencing factors included country of practice, clinical setting, and years of experience. The rationale provided for when to offer predictive genetic testing is encompassed by the ethical principles of beneficence, non-maleficence, autonomy, and informed consent. In conclusion, significant practice variation exists among cardiac genetic counselors regarding predictive genetic testing for children at risk of an inherited cardiomyopathy. These variations call for more research in the area to assist with the development of evidence-based guidelines.
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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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".