A cross-sectional study of ethnicity-based differences in treatment seeking for symptoms of acute coronary syndrome
Bibliographic record
Abstract
BACKGROUND: Patient-related delays in acquiring medical care for symptoms of acute coronary syndrome remain unacceptably long. Many clinical and sociodemographic characteristics associated with treatment-seeking delay are known; however, ethnicity has not been extensively evaluated. OBJECTIVE: The purpose of this study was to examine ethnicity-based differences in the time-to-treatment-seeking intervals of patients experiencing symptoms of acute coronary syndrome. METHOD: Data for this descriptive study were collected for the larger Acute Coronary Syndrome Care in Emergency Departments (ASCEND) study. The larger study is a prospective, observational study in which patients presenting to hospital emergency departments and triaged as having symptoms suggestive of acute coronary syndrome are identified. The primary outcome of this study, the time-to-treatment-seeking interval, was defined as the time between symptom onset and treatment seeking. The predictor variable, ethnicity, was measured with self-reported data and categorised as Chinese, South Asian, or 'Other' ethnic group. Participants in the 'Other' ethnic group were predominantly of European ancestry. Univariate and multivariate analyses were undertaken, along with nonparametric testing. RESULTS: The study sample consisted of 419 participants: 36 Chinese, 126 South Asian, and 257 'Other' participants. The median time-to-treatment-seeking interval, for the total sample, was 180 minutes. A Kruskal-Wallis test demonstrated no statistically significant differences in the time-to-treatment-seeking intervals by ethnicity. CONCLUSION: No ethnicity-based differences in the time-to-treatment-seeking intervals for symptoms of acute coronary syndrome were found. It is possible that Chinese and South Asian patients living in western countries are more aware of the potential signs and symptoms of acute coronary syndrome or feel more confident to access healthcare services than they have been previously.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".