Coronary Artery Disease: Association Study of 5 Loci with Angiographic Indices of Disease Severity
Bibliographic record
Abstract
Background. Different common gene variants were related to coronary artery disease (CAD) in many studies. Yet, the relation of these loci to the severity of CAD is not completely elucidated. Methods. We enrolled 520 subjects (315 CAD cases and 205 controls). CAD presence and extension were assessed by coronary angiography (CAG). Genotyping of five SNPs (namely, rs2230806 (1051G > A) in ABCA1 on chromosome 9, rs2075291 (553G > T) in ApoA5 on chromosome 11, rs320 in LPL on chromosome 8 intron (T → G at position 481), rs10757278 (c.22114477A > G), and rs2383206 (c.22115026 A > G) on chromosome 9p21 locus) was performed by allele-specific PCR. The degree and site of arterial lesions were used to classify patients, tested for association with CAD severity, and related to allele dosage. Results. The polymorphisms rs2383206 and rs10757278 showed significant associations with 2- and 3-vessel coronary disease (p =0.003 and 0.006, respectively). The homozygous GG genotypes of rs10757278 was associated with higher frequency of left anterior descending (LAD), right coronary artery (RCA) and left circumflex (LCX) diseases (p =0.002, 0.016 and 0.002, respectively). The GG genotypes of rs2383206 were found in higher percentage in patients with left main (LM) trunk and left circumflex (LCX) diseases ( p = 0.013 and 0.002, respectively). Conclusion. SNPs rs10757278 and rs2383206 allele dosage could predict CAD severity in the Saudi Arab population.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".