Genetics of Coronary Artery Disease in the 21st Century
Bibliographic record
Abstract
Coronary artery disease (CAD) is still the number-one killer in the world, and clinical trials indicate that it is preventable. Mortality and morbidity can be reduced by at least 30% to 40% by treating known risk factors. Genetic susceptibility is claimed to account for 50% of predisposition. The challenge of preventing CAD in this century, as claimed by some investigators, will require a more comprehensive prevention and treatment of environmental and genetic risk factors. Part of that challenge has been met by genome-wide association studies, which have identified 36 genetic variants with increased risk for CAD. All of these genetic variants have reached genome-wide significance (5 × 10(-8) ) and replicate in independent populations with large sample sizes. More than 50% of these variants occur in >50% of the population, with 10 occurring in >75% of the population. The challenge and the opportunity lie in the observation that >66% of these risk variants do not mediate their risk through known conventional risk factors. These results suggest that genetic predisposition for CAD is conferred by common DNA variants and many factors contributing to the pathogenesis of CAD are yet to be determined. Comprehensive prevention of CAD will most likely require combating genetic and environmental risk factors. We are on the cusp of genetic screening, and new therapeutic targets are becoming available to manage both genetic and environmental risk factors 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".