Abstract 192: Does Neighborhood Matter? How Location Influences Cardiac Arrest Outcomes and Predictability of the Utstein Factors
Bibliographic record
Abstract
Introduction: The Utstein factors only predict 72% of out-of-hospital cardiac arrest (OHCA) survival variability (AUC: 0.86). Previous studies demonstrated geographical variations in both OHCA incidence and outcomes, while others have related these differences to ethnic and socioeconomic measures. The objective of this study was to determine the role of neighborhoods in OHCA survival and predictability in a Canadian setting. Hypothesis: We hypothesized that there are neighbourhood factors associated with survival and variability in cardiac arrest outcomes. Methods: All treated OHCA between April 2006 and March 2010 were mapped into census tracts (CT) using Geographic Information System analysis. Based on their home address, patients were assigned neighborhood factors extracted from the 2005 Canadian Census and include: instability, dependency, deprivation, ethnic diversity, crime rate and density of family physicians. Hierarchical logistic modeling was performed to assess which factors were associated with survival. Results: A total of 4,626 OHCA were eligible for enrollment and contained the required information for geographic mapping. Survival to hospital discharge was 4.3%. The CT survival varied between 0 - 50% (IQR: 0.0 - 7.7), where 68.4% had 0% survival. The Utstein factors predicted 85.4% (95% CI: 81.0 - 89.6%) of survival variability. After adjusting for the Utstein factors, only the ethnic concentration of the CT was associated with survival. Inclusion of CT variables increased the predictability of the Utstein factors to 87.2% (95% CI: 83.4 - 91.2%, p = 0.12). Conclusions: In conclusion, although neighborhood differences in OHCA outcomes exist, this difference contributes minimally to the Utstein factors prediction of survival. Since bystander resuscitation efforts are the most powerful predictors of survival perhaps neighborhood differences in bystander resuscitation efforts may address the gap in the Utstein predictability.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".