Establishment of Specialized Clinical Cardiovascular Genetics Programs: Recognizing the Need and Meeting Standards: A Scientific Statement From the American Heart Association
Bibliographic record
Abstract
Cardiovascular genetics is a rapidly evolving subspecialty within cardiovascular medicine, and its growth is attributed to advances in genome sequencing and genetic testing and the expanding understanding of the genetic basis of multiple cardiac conditions, including arrhythmias (channelopathies), heart failure (cardiomyopathies), lipid disorders, cardiac complications of neuromuscular conditions, and vascular disease, including aortopathies. There have also been great advances in clinical diagnostic methods, as well as in therapies to ameliorate symptoms, slow progression of disease, and mitigate the risk of adverse outcomes. Emerging challenges include interpretation of genetic test results and the evaluation, counseling, and management of genetically at-risk family members who have inherited pathogenic variants but do not yet manifest disease. With these advances and challenges, there is a need for specialized programs combining both cardiovascular medicine and genetics expertise. The integration of clinical cardiovascular findings, including those obtained from physical examination, imaging, and functional assessment, with genetic information allows for improved diagnosis, prognostication, and cascade family testing to identify and to manage risk, and in some cases to provide genotype-specific therapy. This emerging subspecialty may ultimately require a new cardiovascular subspecialist, the genetic cardiologist, equipped with these combined skills, to permit interpretation of genetic variation within the context of phenotype and to extend the utility of genetic testing. This scientific statement outlines current best practices for delivering cardiovascular genetic evaluation and care in both the pediatric and the adult settings, with a focus on team member expertise and conditions that most benefit from genetic evaluation.
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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 | 0.117 | 0.145 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.042 | 0.064 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".