A Journey through Genetic Architecture and Predisposition of Coronary Artery Disease
Bibliographic record
Abstract
INTRODUCTION: To halt the spread of coronary artery disease (CAD), the number one killer in the world, requires primary prevention. Fifty percent of all Americans are expected to experience a cardiac event; the challenge is identifying those at risk. 40 to 60% of predisposition to CAD is genetic. The first genetic risk variant, 9p21, was discovered in 2007. Genome-Wide Association Studies has since discovered hundreds of genetic risk variants. The genetic burden for CAD can be expressed as a single number, Genetic Risk Score (GRS). Assessment of GRS to risk stratify for CAD was superior to conventional risk factors in several large clinical trials assessing statin therapy, and more recently in a population of nearly 500,000 (UK Biobank). Studies were performed based on prospective genetic risk stratification for CAD. These studies showed that a favorable lifestyle was associated with a 46% reduction in cardiac events and programmed exercise, a 50% reduction in cardiac events. Genetic risk score is superior to conventional risk factors, and is markedly attenuated by lifestyle changes and drug therapy. Genetic risk can be determined at birth or any time thereafter. CONCLUSION: Utilizing the GRS to risk stratify young, asymptomatic individuals could provide a paradigm shift in the primary prevention of CAD and significantly halt its spread.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".