Racial and ethnic disparities in bystander resuscitation for out-of-hospital cardiac arrests
Bibliographic record
Abstract
INTRODUCTION: Bystander-provided cardiopulmonary resuscitation (CRP) influences the survival rates of out-of-hospital cardiac arrests (OHCAs). Disparities on bystander resuscitation measures between Black, Hispanic, Asians and Non-Hispanic White OHCAs is unclear. Examining racial and ethnic differences in bystander resuscitations is essential to better target interventions. METHODS: 15,542 witnessed OHCAs were identified between April 1, 2011, and June 30, 2015 using the Resuscitation Outcomes Consortium Epidemiologic Registry 3, a multi-center, controlled trial about OHCAs in the United States and Canada. Multivariable logistic regression model was used to analyze the differences in bystander resuscitation (bystander CRP [B-CPR], CPR plus ventilation, automated external defibrillators/defibrillator application [B-AED/D], or delivery of shocks) and clinical outcomes (death at the scene or en route, return of spontaneous circulation upon first arrival at the emergency department [ROSC-ED], survival until ED discharge [S-ED], survival until hospital discharge [S-HOS], and favorable neurological outcome at discharge) between Black, Hispanic, or Asian victims and Non-Hispanic White victims. RESULTS: Compared to OHCA victims in Non-Hispanic Whites, Black, Hispanic, and Asians were less likely to receive B-CPR (adjusted OR: 0.79; 95 % CI: 0.63-0.99), and B-AED/D (adjusted OR: 0.80; 95 % CI: 0.65-0.98) in public locations. And, Black, Hispanic, and Asian OHCAs were less likely to receive bystander resuscitation in street/highway locations and public buildings, and less likely to have better clinical outcomes, including ROSC-ED, S-ED and S-HOS. CONCLUSION: Black, Hispanic and Asian victims with witnessed OHCAs are less likely to receive bystander resuscitation and more likely to get worse outcomes than Non-Hispanic White victims.
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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 | 0.000 | 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) | 0.003 | 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".