The relationship between race and emergency medical services resuscitation intensity for those in refractory-arrest
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Previous studies have reported race-based health disparities in North America. It is unknown if emergency medical service (EMS) treatment of out-of-hospital cardiac arrest (OHCA) varies based on race. We sought to compare markers of resuscitation intensity among different racial groups. Methods: Using data of adult EMS-treated OHCAs from the Trial of Continuous or Interrupted Chest Compressions During CPR, we analyzed data from participants for whom on-scene return of spontaneous circulation (ROSC) was not achieved. We fit multivariate regression models using a generalized estimating equation, to estimate the association between patient race (White vs. Black vs. "Other") and the following markers for resuscitation intensity: (1) resuscitation attempt duration; (2) intra-arrest transport; (3) number of epinephrine doses; (4) EMS arrival-to-CPR interval, and (5) 9-1-1 to first shock. Results: From our study cohort of 5370 cases, the median age was 65 years old (IQR: 53-78), 2077 (39 %) were women, 2121 (39 %) were Black, 596 (11 %) were "Other race", 2653 (49 %) were White, and 4715 (88 %) occurred in a private location. With reference to White race, Black race was associated with a longer resuscitation attempt duration and a lower number of epinephrine doses; Black and "Other" race were both associated with a lower odds of intra-arrest transport. Conclusion: We identified race-based differences in EMS resuscitation intensity for OHCA within a North American cohort, although 40% of race data was missing from this dataset. Future research investigating race-based differences in OHCA management may be warranted.
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.
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 | 0.003 |
| 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) | 0.000 | 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 it