Abstract 29: Does Neighborhood Really Matter? How Where One Lives Influences Their Outcome Following Out-of-Hospital Cardiac Arrest
Bibliographic record
Abstract
Introduction: Traditional Utstein factors for an out of hospital cardiac arrest (OHCA) have been extensively investigated, with numerous studies evaluating economic status for association with survival. Few studies have evaluated neighbourhood factors collectively as determinants of cardiac arrest outcomes. This will be the first multifactorial Canadian to investigate neighbourhood factors as determinants of OHCA outcomes. Hypothesis: We hypothesized that within 140 neighbourhoods in Toronto, Canada, there are neighbourhood level factors associated with survival to hospital discharge after an OHCA. Methods: A retrospective, observational study of all OHCA treated by Toronto EMS between April 2006 and March 2010. Using Geographic Information System (GIS) analysis, OHCA patients were assigned to a neighbourhood based on their residential address. Neighbourhood survival rates were calculated. Results: A total of 4408 OHCA were eligible for enrollment and contained the necessary information for GIS mapping. The mean (SD) age was 69.7 (16.4) y; 64% were male. The response time was 6.3 (3.6) min, 41.9% received bystander CPR, 16.8% occurred in a public location, 20.7% presented in a shockable rhythm, 27.7% had a ROSC and 5.3% survived to hospital discharge. Survival rates varied across all neighbourhoods: 25% of neighbourhoods had a 0% survival rate, while some had rates as high as 25% (See Figure). Conclusions: In conclusion, differences in survival following OHCA vary by neighbourhood, suggesting that a patient's home neighbourhood plays a role in outcomes. Further analyses are underway to investigate which neighbourhood factors are associated with improved survival.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".