Genetic stratification for primary prevention of coronary artery disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: To review the literature showing genetic risk variants is a reliable means of stratifying for risk of CAD for primary prevention. RECENT FINDINGS: Over 90 genetic risk variants have been discovered that predispose to CAD. Results of several studies show that these risk variants effectively stratify for risk of CAD in asymptomatic individuals. SUMMARY: The total individual genetic risk can be summarized into a single number referred to as the genetic risk score (GRS). The GRS unlike the Framingham Risk Score is not dependent on age and independent of conventional risk factors. As DNA does not change during one's lifetime the GRS can be calculated at birth or any time thereafter. Furthermore, the GRS has been shown to provide superior discriminatory power in selecting individuals who will benefit most from lifestyle changes or statin therapy. A prospective study showed individuals with high GRS and a favorable lifestyle was associated with significant reduction of cardiac events compared with an unfavorable lifestyle. Furthermore, the study shows inherited risk can be reduced analogous to reduction of risk form acquired and environmental factors. The use of GRS to stratify for risk of CAD in asymptomatic individuals could transform primary prevention worldwide.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".