Examining the association between ethnicity and out-of-hospital cardiac arrest interventions in Salt Lake City, Utah
Bibliographic record
Abstract
Aims: Previous research has reported racial disparities in out-of-hospital cardiac arrest (OHCA) interventions, including bystander CPR and AED use. However, studies on other prehospital interventions are limited. The primary objective of this study was to investigate race/ethnic disparities in out-of-hospital cardiac arrest (OHCA) interventions: EMS response times, medication administration, and decisions for intra-arrest transport. The secondary objective was to evaluate differences in the provision of Bystander CPR (CPR) and application of AED. Methods: We retrospectively analyzed data from the Salt Lake City Fire Department (2010-2023). We included adults 18 years or older with EMS-treated OHCA. Race/ethnicity was categorized as White people, Asian people, Black people, Hispanic people, and others. We employed multivariable regression analysis to evaluate the association between race/ethnicity and the outcomes of interest. Results: Unadjusted analyses revealed no significant differences across ethnic groups in EMS response, medication administration, bystander CPR, or intra-arrest transport decisions. However, significant ethnic disparities were observed in Automated External Defibrillator (AED) utilization, Black people having the lowest rate (6.5%) and Asian people the highest (21.8%). The adjusted analysis found no significant association between race/ethnicity and all OHCA intervention measures, nor between race/ethnicity and survival outcomes. Conclusions: Our multivariable analysis found no statistically significant association between race/ethnicity and EMS response time, epinephrine administration, antiarrhythmic medication use, bystander CPR, AED intervention, or intra-arrest transport. These results imply regional variations in ethnic disparities in OHCA may not be consistent across all areas, warranting further research into disparities in other regions and additional influential factors like neighborhood conditions and socioeconomic status.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".