Abstract 306: Out-of-hospital Cardiac Arrest Response Characteristics Moderate the Effect of Response Time on Survival
Bibliographic record
Abstract
Background: Research has shown that each minute delay in response time reduces survival from OHCA. Although Utstein variables like public location, witnessed, bystander CPR, and bystander AED shock are known to independently improve survival, how they moderate the effect of response time delays on survival is unknown. Methods: We included OHCAs from the Resuscitation Outcomes Consortium Epistry-Cardiac Arrest database from December 1, 2005 to June 30, 2015. We included all adult, non-traumatic, non-EMS witnessed, and EMS-treated OHCA episodes. We used a logistic regression model to estimate survival to hospital discharge as a function of response time. We adjusted for standard Utstein variables and included interaction terms between response time and public location, bystander witnessed, bystander CPR, and bystander AED shock. With four binary interacted variables, there were a total of sixteen subpopulations, each with a different effect of response time on survival. Results: 83,275 patients were included (15% public, 45% witnessed, 47% CPR, 2% AED shock). Across the 10 subpopulations that comprise 99%+ of the data, a one-minute delay in response time reduced the odds of survival from 1.7% to 10.9%, depending on the arrest characteristics. All interaction tests for effect modification were significant. The reduction in odds of survival was largest for witnessed arrests (OR=0.961; 95% CI: 0.944-0.978), followed by arrests with bystander CPR (OR=0.965; 95% CI: 0.948-0.982) and in public locations (OR=0.978; 95% CI: 0.960-0.996). In contrast, a one-minute delay for arrests with bystander AED shock (OR=1.086; 95% CI: 1.058-1.114) increased the odds of survival. Conclusions: Utstein predictors significantly moderate the effect of response time on survival. Arrests that are witnessed, public location, and/or receive bystander CPR are negatively affected by slower response time. Arrests with a bystander AED shock are not sensitive to response time delays.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".