A customized genetic approach to the number one killer: coronary artery disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: To review the evidence supporting genetic predisposition to coronary artery disease (CAD). Secondly, to elucidate the barriers precluding the identification of genes responsible for CAD. Thirdly, to indicate the new technology now available to overcome these barriers and summarize current progress. RECENT FINDINGS: Evidence strongly supports that 50% of susceptibility to CAD is genetic. Prevention of CAD requires comprehensive genetic and risk factor modification. Technology to perform genome-wide association studies became available in 2005, namely, the microarrays with 500,000 and 1 million single nucleotide polymorphisms as DNA markers for high-throughput genotyping to determine gene frequencies in large datasets of cases and controls. The first genetic variant, 9p21, for CAD was identified in the Ottawa Heart Genomic study. This is not only a genetic risk factor but also independent of other known risk factors for CAD. 9p21 was subsequently confirmed as a risk variant in several other independent studies involving 64 000 Caucasians. 9p21 increases the risk of CAD by 40% and 20% in heterozygous or homozygous forms respectively. It occurs in 75% of Caucasians, and has recently been confirmed in several other ethnic groups. SUMMARY: Thus, identification of predisposition to CAD is well underway with genome-wide association studies and the first common genetic risk variant, 9p21, has been identified.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".