The Impact of Race on the Acute Management of Chest Pain
Bibliographic record
Abstract
African Americans with acute coronary syndromes receive cardiac catheterization less frequently than whites. The objective was to determine if such disparities extend to acute evaluation and non interventional treatment.Data on adults with chest pain (N = 7,935) presenting to eight emergency departments (EDs) were evaluated from the Internet Tracking Registry of Acute Coronary Syndromes. Groups were selected from final ED diagnosis: 1) acute myocardial infarction (AMI), n = 400; 2) unstable angina/non-ST-elevation myocardial infarction (UA/NSTEMI), n = 1,153; and 3) nonacute coronary syndrome chest pain (non-ACS CP), n = 6,382. American College of Cardiology/American Heart Association guidelines for AMI and UA/NSTEMI were used to evaluate racial disparities with logistic regression models. Odds ratios (ORs) were adjusted for age, gender, guideline publication, and insurance status. Non-ACS CP patients were assessed by comparing electrocardiographic (ECG)/laboratory evaluation, medical treatment, admission rates, and invasive and noninvasive testing for coronary artery disease (CAD).African Americans with UA/NSTEMI received glycoprotein IIb/IIIa receptor inhibitors less often than whites (OR, 0.41; 95% CI = 0.19 to 0.91). African Americans with non-ACS CP underwent ECG/laboratory evaluation, medical treatment, and invasive and noninvasive testing for CAD less often than whites (p < 0.05). Other nonwhites with non-ACS CP were admitted and received invasive testing for CAD less often than whites (p < 0.01). African Americans and other nonwhites with AMI underwent catheterization less frequently than whites (OR, 0.45; 95% CI = 0.29 to 0.71 and OR, 0.40; 95% CI = 0.17 to 0.92, respectively). A similar disparity in catheterization was noted in UA/NSTEMI therapy (OR, 0.53; 95% CI = 0.40 to 0.68 and OR, 0.68; 95% CI = 0.47 to 0.99).Racial disparities in acute chest pain management extend beyond cardiac catheterization. Poor compliance with recommended treatments for ACS may be an explanation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".