Predictive genetic testing for coronary artery disease
Bibliographic record
Abstract
Coronary artery disease (CAD) is an inflammatory-metabolic disease in which atherosclerotic plaques cause stenosis of the coronary arteries, leading to acute clinical complications such as myocardial infarction. Since CAD is a multifactorial, polygenic disease with a substantial environmental component, individual risk prediction and stratification is often difficult. Recent technological advances have resulted in substantial progress elucidating the impact of common genetic variation on CAD progression. The discovery of common genetic variants, including the chromosome 9p21.3 locus as the strongest and most highly replicated independent genetic CAD risk factor, has stimulated interest in genetic testing for CAD-associated risk variants. The ultimate goal of genotype-based CAD risk prediction is to improve upon the discrimination and stratification offered by conventional risk factors alone. Genotype-based CAD risk prediction may eventually have clinical utility, but not without intrinsic complexities. Are genotype variables superior predictors of risk compared to a family history of CAD? Is a 10-year risk prediction window ambitious enough for the predictive power of genotype data? This review will outline the current state of genotype-based CAD risk prediction and highlight challenging issues integral to the successful implementation of genetic testing for CAD.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".