Abstract 89: Does Neighborhood Really Matter? How Location Influences Bystander CPR Rates
Bibliographic record
Abstract
Introduction: Numerous studies have investigated determinants of bystander cardiopulmonary resuscitation (CPR) for out-of-hospital cardiac arrest (OHCA) patients. Socioeconomic status and ethnicity have been linked to who receives bystander CPR, and recent studies have showed geographical differences in CPR rates. Research from other disciplines has suggested that location is a strong determinant of health and health related interventions and outcomes. Therefore, the objective of this study was to determine the role of neighborhoods in bystander CPR rates in a Canadian population. Hypothesis: We hypothesized that neighborhood factors are associated with bystander CPR rates. Methods: All treated OHCA within the City of Toronto between April 2006 and March 2010 were included. Geographic Information Systems were used to assign patients to census tracts (CT) based on their pick-up address and linked to neighborhood factors (instability, dependency, deprivation, ethnic diversity, crime rate and density of family physicians). Bivariate and hierarchical logistic modeling was performed to assess which factors were associated with bystander CPR. Results: A total of 5,139 OHCA were eligible for enrollment and contained the required information for geographic mapping. The bystander CPR rate was 41.1%. The bystander CPR rate varied across census tracts (0 - 92%, IQR: 25.0 - 50.0%), where 10% had a bystander CPR rate lower than 15%. The CTs deprivation and density of family physicians were associated with bystander CPR (see table). After adjusting for individual characteristics, no CT level variables were associated with bystander CPR. Conclusion: Deprivation and the density of family physicians were associated with higher rates of bystander CPR. However, after adjustment for patient characteristics, neighborhoods were not associated with CPR. Future research should explore the relationship between location and witnessed status and its role in bystander CPR.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".