Racial Disparities in Acute Coronary Syndrome Management Within a Universal Healthcare Context: Insights From the AMI-OPTIMA Trial
Bibliographic record
Abstract
Background Although prior studies have demonstrated racial disparities regarding acute coronary syndrome (ACS) care within private or mixed healthcare systems, few researchers have explored such disparities within universal healthcare systems. We aimed to evaluate the quality and outcomes of in-hospital ACS management for White patients vs patients of colour, within a universal healthcare context. Methods We performed a post hoc analysis of the A cute M yocardial I nfarction - Knowledge Translation to Optim ize A dherence to Evidence-Based Therapy study, a cluster-randomized trial evaluating a knowledge-translation intervention at 24 hospitals in Quebec, Canada (years: 2009 and 2012). The primary endpoint was coronary catheterization. The secondary endpoints included in-hospital mortality, percutaneous and surgical coronary revascularization, major bleeding, total stroke, and discharge prescription of evidence-based medical therapy. Results Of 3444 included patients, 2738 were White, and 706 were people of colour. The mean age was 68.2 years (33.3% women) among White patients and 69.5 years (36.0% women) among patients of colour. Patients of colour were less likely to undergo in-hospital coronary catheterization than were White patients (74.5% vs 80.3%, P = 0.001). This difference was attenuated after adjusting for patient-level characteristics (odds ratio 0.89; 95% confidence interval 0.73-1.09), and it was eliminated after adjusting for hospital-level characteristics (odds ratio 1.04; 95% confidence interval 0.73-1.49). Conclusions Racial disparity in coronary catheterization for ACS persists within a universal healthcare context. Patients' comorbidities and hospital-level factors may be partially responsible for this inequality. Future research on cardiovascular healthcare in patients with diverse racial/ethnic backgrounds in universal healthcare systems is needed to remediate racial inequality in ACS management.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".