Association of Race and Ethnicity With Obstructive Coronary Artery Disease
Bibliographic record
Abstract
Background: Appropriate selection of patients with stable coronary artery disease (CAD) for coronary angiography is dependent on the pretest probability of obstructive CAD; however, little is known about the potential differences in CAD by race and ethnic groups. Objectives: The purpose of this study was to evaluate the association of race and ethnicity with coronary obstruction in stable CAD. Methods: We evaluated first coronary angiography for CAD evaluation between 2012 and 2019 in Ontario, Canada. Race and ethnicity were identified by physicians. The main outcome was the rate of obstructive CAD (left main stenosis ≥50% or major epicardial vessel stenosis ≥70%). Multivariable logistic regression analyses evaluated the independent association of race and ethnicity with CAD. Results: Among 71,199 CAD patients, 14.0% were South Asian (SA), 4.4% were East Asian (EA), and 58,131 were White patients. SA patients were the youngest at 60.9 years vs 62.4 years for EA patients and 65.1 years for White patients but were most likely to have obstructive CAD (46.9%) (EA 43.0% and White patients 37.9%). SA patients had the highest prevalence of 3-vessel CAD at 13.4% (vs 12.5% in EA and 7.7% in White patients). The adjusted odds ratio was 67% higher (1.67; 95% CI: 1.59 to 1.75) for having obstructive CAD in SA patients than that in White patients. EA patients also had significantly increased adjusted odds of obstructive CAD compared with White patients (1.40; 95% CI: 1.29-1.52). Conclusions: SA patients were younger at presentation but had the highest adjusted odds of obstructive CAD. Incorporation of race and ethnicity information may improve risk-prediction tools for detection of coronary obstruction.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".