https://researchopenworld.com/comprehensive-risk-genetic-and-acquired-stratification-for-primary-prevention-of-cad-genetic-risk-of-cad/#
Bibliographic record
Abstract
Coronary Artery Disease (CAD) is the number one cause of death in the world.Epidemiologists' claim 50 percent of CAD is genetic.The identified first genetic risk variant, 9p21, was discovered in 2007, and subsequently, efforts have led to identifying hundreds of genetic risk variants predisposing to CAD.CAD is preventable based on clinical trials showing reduction in conventional risk factors, such as cholesterol, is associated with 30-40 percent reduction in cardiac mortality and events.Statin therapy, which lowers plasma cholesterol, is very safe and effective.About 50 percent of all Americans living a normal lifespan will experience a cardiac event.The challenge is selecting among asymptomatic individuals, the 50 percent who would benefit most from prevention.Conventional risk factors are age-dependent, while genetic risk variants are independent of age, and can be determined anytime from birth on, since one's DNA does not change in one's lifetime.Utilizing a microarray containing the genetic risk variants, and DNA from saliva or blood, studies was performed in over 1 million cases and controls.Genetic risk variants were shown to be relatively independent of conventional risk factors and offer greater discriminatory power in stratifying for CAD risk.Individuals with the highest genetic risk score (GRS) had the highest risk for CAD and benefitted most from statin therapy.A recent study employed the genetic risk in a sample size of 55,685 individuals.Those with a high GRS for CAD (20%) had a 91 percent higher risk for cardiac events.Individuals with a healthy lifestyle and high GRS had a 46 percent lower risk for cardiac events in comparison to those with an unfavorable lifestyle thus, genetic risk can be reduced.Utilizing the GRS to risk stratify for primary prevention of CAD will represent a paradigm shift in halting the spread of this pandemic disease.
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.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.638 | 0.591 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".