Promise and Peril of a Genotype‐First Approach to Mendelian Cardiovascular Disease
Bibliographic record
Abstract
Precision medicine, which among other aspects includes an individual's genomic data in diagnosis and management, has become the standard-of-care for Mendelian cardiovascular disease (CVD). However, early identification and management of asymptomatic patients with potentially lethal and manageable Mendelian CVD through screening, which is the promise of precision health, remains an unsolved challenge. The reduced costs of genomic sequencing have enabled the creation of biobanks containing in-depth genetic and health information, which have facilitated the understanding of genetic variation, penetrance, and expressivity, moving us closer to the genotype-first screening of asymptomatic individuals for Mendelian CVD. This approach could transform health care by diagnostic refinement and facilitating prevention or therapeutic interventions. Yet, potential benefits must be weighed against the potential risks, which include evolving variant pathogenicity assertion or identification of variants with low disease penetrance; costly, stressful, and inappropriate diagnostic evaluations; negative psychological impact; disqualification for employment or of competitive sports; and denial of insurance. Furthermore, the natural history of Mendelian CVD is often unpredictable, making identification of those who will benefit from preventive measures a priority. Currently, there is insufficient evidence that population-based genetic screening for Mendelian CVD can reduce adverse outcomes at a reasonable cost to an extent that outweighs the harms of true-positive and false-positive results. Besides technical, clinical, and financial burdens, ethical and legal aspects pose unprecedented challenges. This review highlights key developments in the field of genotype-first approaches to Mendelian CVD and summarizes challenges with potential solutions that can pave the way for implementing this approach for clinical care.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".