Abstract 174: Cardiotoxic Neighborhoods: Marked Variations in Domiciles of Victims of Cardiac Arrest Within a Large Urban Area
Bibliographic record
Abstract
Introduction: Out of hospital cardiac arrest (OHCA) incidence rates may vary across regions. Prior studies measured OHCA variability by the location of the OHCA, rather than the victim's place of residence, thereby omitting potentially important demographic predictors of OHCA. Objectives: To measure variability of OHCA rates by residential address within a large urban centre, and the characteristics of persons living in areas that are associated with a higher rate of OHCA. Methods: We included 5656 adults aged 20+ y who experienced an OHCA in Toronto, Canada from 2006-2010. Each OHCA patient's home address was spatially mapped to 1 of 527 census tracts (CT). In order to assess variability we conducted (1) point cluster analysis using SatScan software, which identified areas with significantly higher counts of OHCA; (2) Poisson regression models were used to assess the OHCA relative risk (RR) by CT (after adjusting for age and sex); (3) We assessed the relationship between OHCA rates and socioeconomic (SES) variables within each CT. Results: Incidence of OHCA varied widely by CT (Figure 1). Adjustment for age and sex did not alter this variation. Cluster analysis identified 4 geographically distinct areas where there was higher risk of OHCA compared to adjacent CTs. With respect to SES variables, CTs with a higher median household income had a lower risk of OHCA (RR 0.98 per $10000 increase in income, 99% CI 0.95-1.00), as did CTs with higher education status (RR 0.996 per 1% increase in education, 99% CI 0.992-0.999). CTs with a higher percentage of South Asians (SA) were not at higher risk of OHCA (RR 1.003 per 1% increase in SA population, 99% CI 0.997-1.009), but those with a higher percentage of Afro-Caribbeans (AC) were (RR 1.007 per 1% increase in AC population, 99% CI 1.002-1.013). Conclusions: The risk of OHCA varies by the features of one's place of residence. Further study is required to investigate possible causes for these variations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".