Racial/Ethnic and Sex Differences in Emergency Medical Services Transport Among Hospitalized US Stroke Patients: Analysis of the National Get With The Guidelines–Stroke Registry
Bibliographic record
Abstract
BACKGROUND: Differences in activation of emergency medical services (EMS) may contribute to racial/ethnic and sex disparities in stroke outcomes. The purpose of this study was to determine whether EMS use varied by race/ethnicity and sex among a current, diverse national sample of hospitalized acute stroke patients. METHODS AND RESULTS: We analyzed data from 398,798 stroke patients admitted to 1613 Get With The Guidelines-Stroke participating hospitals between October 2011 and March 2014. Multivariable logistic regression was used to evaluate the associations between combinations of racial/ethnic and sex groups with EMS use, adjusting for potential confounders including demographics, medical history, and stroke symptoms. Patients were 50% female, 69% white, 19% black, 8% Hispanic, 3% Asian, and 1% other, and 86% had ischemic stroke. Overall, 59% of stroke patients were transported to the hospital by EMS. White women were most likely to use EMS (62%); Hispanic men were least likely to use EMS (52%). After adjustment for patient characteristics, Hispanic and Asian men and women had 20% to 29% lower adjusted odds of using EMS versus their white counterparts; black women were less likely than white women to use EMS (odds ratio 0.75, 95% CI 0.72 to 0.77). Patients with weakness or paresis, altered level of consciousness, and/or aphasia were significantly more likely to use EMS than patients without each symptom; the observed racial/ethnic and sex differences in EMS use remained significant after adjustment for stroke symptoms. CONCLUSIONS: EMS use differed by race/ethnicity and sex. These contemporary data document suboptimal use of EMS transport among US stroke patients, especially by racial/ethnic minorities and those with less recognized stroke symptoms.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".