Establishment of Specialized Clinical Cardiovascular Genetics Programs: Recognizing the Need and Meeting Standards: A Scientific Statement From the American Heart Association
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it