Susceptibility genes for coronary heart disease and myocardial infarction
Bibliographic record
Abstract
Coronary heart disease and its main complication, myocardial infarction is leading cause of death worldwide. Over the past years, much progress has been made in the pharmacotherapy of major risk factors like dyslipidemias, diabetes mellitus and hypertension. The targeting of coronary risk factors coupled with advances in the management of coronary artery disease has improved patient survival. However, the incidence of cardiovascular disease is projected to continue to rise and the identification of individuals at risk should improve beyond the traditional models of global risk factor scoring. In the past few years, important progresses have been made in the area of genomics, especially with the completion of the human genome-sequencing Consortium of 2004, proteomics and imaging. This progress will promote a better understanding of cardiovascular risk assessments and disease prediction, thus allowing earlier preventive strategies to prevent and improve cardiovascular outcomes. These genomic advances have improved characterization of disease pathology especially at the molecular level with the discovery and introduction of genetic markers, single nucleotide polymorphisms (SNPs), and haplotype blocks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".