Abstract 18723: Socioeconomic Disparities and Burden of Sudden Cardiac Arrest in Metropolitan Areas: A USA-Canada Comparative Analysis
Bibliographic record
Abstract
Introduction: Low socioeconomic status (SES) is associated with poor cardiovascular outcomes and may be a determinant of sudden cardiac arrest (SCA) incidence. Objective: We evaluated the association between SES and SCA incidence from seven sites in the Resuscitation Outcomes Consortium (ROC) Epistry, a registry of all out-of-hospital cardiac arrests assessed by emergency medical services personnel, and tested whether the relationship differed between the US and Canada. Methods: Cases were primary cardiac arrests occurring in a residence in Dallas, Pittsburgh, Portland, and Seattle-King County ROC sites (US); and Ottawa, Toronto, and Vancouver sites (Canada), 4/1/2006 - 3/31/2007. Each case was linked to the census tract in which the arrest occurred. Census tracts were classified into quartiles of median income, and incidence per quartile was calculated using all SCAs occurring in census tracts in that quartile (numerator), and the population of all census tracts in that quartile (denominator). Poisson regression was used to estimate and compare SCA incidence rate ratios (IRRs) in the lowest vs. highest quartile of SES. Results: Across the 9,235 total SCAs (range 638-2,586 per site; mean ages 64-70 years; males 58-65%), incidence was nearly double in the lowest vs. highest SES quartile (IRR 1.9 [95% CI 1.8-2.0]). This disparity was stronger in those Conclusions: SCA incidence was consistently higher in poorer neighborhoods of North American metropolitan areas, though this relationship was attenuated in Canadian cities. These findings underscore the important association of SES with SCA incidence, also suggesting that fundamental differences between the two countries may contribute to the greater adverse impact of SES in the US.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".