Sex disparities in the presentation, management and outcomes of patients with acute coronary syndrome: insights from the ACS QUIK trial
Bibliographic record
Abstract
AIMS: Our aim was to explore sex differences and inequalities in terms of medical management and cardiovascular disease (CVD) outcomes in a low/middle-income country (LMIC), where reports are scarce. METHODS: We examined sex differences in presentation, management and clinical outcomes in 21 374 patients presenting with acute coronary syndrome (ACS) in Kerala, India enrolled in the Acute Coronary Syndrome Quality Improvement in Kerala trial. The main outcomes were the rates of in-hospital and 30-day major adverse cardiovascular events (MACEs) defined as composite of death, reinfarction, stroke and major bleeding. We fitted log Poisson multivariate random effects models to obtain the relative risks comparing women with men, and adjusted for clustering by centre and for age, CVD risk factors and cardiac presentation. RESULTS: A total of 5191 (24.3%) patients were women. Compared with men, women presenting with ACS were older (65±12 vs 58±12 years; p<0.001), more likely to have hypertension and diabetes. They also had longer symptom onset to hospital presentation time (median, 300 vs 238 min; p<0.001) and were less likely to receive primary percutaneous coronary intervention for ST-elevation myocardial infarction (45.9% vs 49.8% of men, p<0.001). After adjustment, women were more likely to experience in-hospital (adjusted relative risk (RR)=1.53; 95% CI 1.32 to 1.77; p<0.001) and 30-day MACE (adjusted RR=1.39; 95% CI 1.23 to 1.57, p<0.001). CONCLUSION: Women presenting with ACS in Kerala, India had greater burden of CVD risk factors, including hypertension and diabetes mellitus, longer delays in presentation, and were less likely to receive guideline-directed management. Women also had worse in-hospital and 30-day outcomes. Further efforts are needed to understand and reduce cardiovascular care disparities between men and women in LMICs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".