Racism Is Not a Factor in Door‐to‐electrocardiogram Times of Patients With Symptoms of Acute Coronary Syndrome: A Prospective, Observational Study
Bibliographic record
Abstract
BACKGROUND: Investigators have identified important racial identity/ethnicity-based differences in some aspects of acute coronary syndrome (ACS) care and outcomes (time to presentation, symptoms, receipt of coronary angiography/revascularization, repeat revascularization, mortality). Patient-based differences such as pathophysiology and treatment-seeking behavior account only partly for these outcome differences. We sought to investigate whether there are racial identity/ethnicity-based variations in the initial emergency department (ED) triage and care of patients with suspected ACS in Canadian hospitals. METHODS: We prospectively enrolled ED patients with suspected ACS from one university-affiliated and two community hospitals. Trained research assistants administered a standardized interview to gather data on symptoms, treatment-seeking patterns, and self-reported racial/ethnic identity: "white," South Asian" (SA), "Asian," or "Other." Clinical parameters were obtained through chart review. The primary outcome was door-to-electrocardiogram (D2ECG) time. ECG times were log-transformed and two linear regression models, controlling for important demographic, system, and clinical factors, were fit. RESULTS: Of 448 participants, 214 (48%) reported white identity, 115 (26%) SA, 83 (19%) Asian, and 36 (8%) "Other." Asian respondents were younger and more likely to report initial discomfort as "low" and be accompanied by family; respondents identifying as "Other" were more likely to report initial discomfort as "high." There was no difference in D2ECG time between white participants and all other groups, but there were statistically significant differences by sex: women had longer D2ECG times than men. Exploring more specific racial identities revealed similar findings: no significant differences between the white, SA, Asian, and other groups, while sex (women had 13.4% [95% confidence interval, 0.81%-27.57%] longer D2ECG times) remained statistically significantly different in the adjusted models. CONCLUSION: Although racial/ethnicity-based differences in aspects of ACS care have been previously identified, we found no differences in the current study of early ED care in a Canadian urban setting. However, female patients experience longer D2ECG times, and this may be a target for process improvements.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".