Ethnic disparities in care and outcomes of non-ST-segment elevation myocardial infarction: a nationwide cohort study
Bibliographic record
Abstract
AIMS: Little is known about ethnic disparities in care and clinical outcomes of patients admitted with non-ST-segment elevation myocardial infarction (NSTEMI) in national cohorts from universal healthcare systems derived from Europe. METHODS AND RESULTS: We identified 280 588 admissions with NSTEMI in the UK Myocardial Infarction National Audit Project (MINAP), 2010-2017, including White patients (n = 258 364) and Black, Asian, and Minority Ethnic (BAME) patients (n = 22 194). BAME patients were younger (66 years vs. 73 years, P < 0.001) and more frequently had hypertension (66% vs. 54%, P < 0.001), hypercholesterolaemia (49% vs. 34%, P < 0.001), and diabetes (48% vs. 24%, P < 0.001). BAME patients more frequently received invasive coronary angiography (80% vs. 68%, P < 0.001), percutaneous coronary intervention (PCI) (52% vs. 43%, P < 0.001), and coronary artery bypass graft surgery (9% vs. 7%, P < 0.001). Following propensity score matching, BAME compared with White patients had similar in-hospital all-cause mortality [odds ratio (OR) 0.91, confidence interval (CI) 0.76-1.06; P = 0.23], major bleeding (OR 0.99, CI 0.75-1.25; P = 0.95), re-infarction (OR 1.15, CI 0.84-1.46; P = 0.34), and major adverse cardiovascular events (MACE) (OR 0.94, CI 0.80-1.07; P = 0.35). CONCLUSION: BAME patients with NSTEMI had higher cardiometabolic risk profiles and were more likely to undergo invasive angiography and revascularization, with similar clinical outcomes as those of their White counterparts. Among the quality indicators assessed, there is no evidence of care disparities among BAME patients presenting with NSTEMI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".