Race‐ and Ethnicity‐Specific Hospital Arrival and Emergency Medicine Service Activation Times by US State for Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND: Delayed hospital arrival after 4.5 hours of stroke onset excludes patients from intravenous thrombolytic therapy. In the United States, prehospital triage is regulated by each state. Understanding race- and ethnicity-specific prehospital delays in each state could guide targeted interventions. METHODS: This cross-sectional study examined adult patients treated at the GWTG (Get With The Guidelines)-Stroke participating hospitals from January 2021 to August 2023 for acute ischemic stroke. The outcomes, including onset-to-arrival >4.5 hours, onset-to-911 call >2.5 hours, and 911 call-to-arrival >1 hour by race and ethnicity and state, were examined using multivariable logistic regression analysis adjusting for patient and hospital-level characteristics. RESULTS: The study included 691 689 patients with a median age of 71 years and 48.6% women. Compared with White patients, risk-adjusted odds of onset-to-arrival >4.5 hours were higher in Asian patients (1.24 [95% CI, 1.20-1.28]), Black patients (1.18 [95% CI, 1.16-1.19]), and Hispanic patients (1.10 [95% CI, 1.07-1.12]); onset-to-911 call >2.5 hours was higher among Black patients (1.21 [95% CI, 1.16-1.26]); and 911 call-to-arrival >1 hour was lower among Asian (0.55 [95% CI, 0.49-0.63]), Black patients (0.67 [95% CI, 0.62-0.72]), and Hispanic patients (0.69 [95% CI, 0.63-0.75]). Relative to Texas, which has the highest racial and ethnic diversity index, the odds of onset-to-arrival >4.5 hours were higher in 20 states for non-White patients and 9 states for White patients. CONCLUSIONS: Delayed hospital arrivals are more prevalent among Asian, Black, and Hispanic patients, but emergency medicine service transportation times are shorter, suggesting the need for culturally tailored community stroke education. A few states have exceedingly high delayed arrival, highlighting an opportunity to improve state-wide stroke readiness and emergency medicine service triage.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".