A Journey through Genetic Architecture and Predisposition of Coronary Artery Disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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