Abstract W MP99: Race/Ethnic and Sex Differences in EMS Transport among Hospitalized U.S. Stroke Patients: Analysis of the National Get With The Guidelines-Stroke Registry
Bibliographic record
Abstract
Background: Calling 911 is the recommended first step when stroke symptoms occur. Differences in activation of emergency medical services (EMS) may contribute to race/ethnic and sex disparities in stroke outcomes. The purpose of this study was to determine whether EMS utilization varies among a contemporary, diverse national sample of hospitalized acute stroke patients. Methods: We analyzed data from 398,798 stroke patients admitted to 1,613 Get With The Guidelines-Stroke participating hospitals from 10/1/11-3/31/14. Multivariable logistic regression was utilized to evaluate the associations between race/ethnic group and sex, with EMS use, adjusting for potential confounders. Results: Patients were 50.4% female, 69% white, 19% black, 8% Hispanic, 3% Asian, 1% other; 85.9% ischemic stroke. Overall 58.6% of stroke patients were transported to the hospital by EMS. EMS utilization differed by sex and race/ethnic group (interaction p<0.001). White females were most likely to use EMS (62.0%) and Hispanic males were least likely to (52.2%). Age, health insurance coverage, and history of prior stroke or TIA varied significantly among race/ethnic groups (p<0.0001). After adjustment for both patient and hospital characteristics, Hispanic and Asian men and women were less likely than their white counterparts to utilize EMS; black females were less likely than white females to utilize EMS (Table). Conclusion: EMS use was low overall and differential by race/ethnicity and sex. These contemporary data support a need for targeted initiatives to increase EMS transport among U.S. stroke patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".