Abstract 16987: Increased Ischemic Heart Disease And Stroke-related Hospitalizations From Cold Temperature in Ontario, Canada: Population-based Study
Bibliographic record
Abstract
Introduction: Experimental studies suggest that exposure to extreme ambient temperature, especially cold, can induce inflammatory reactions and a state of hypercoagulability which may in turn promote thrombosis and clot formation. However, epidemiological evidence relating cold temperature and cardiovascular-related morbidity is sparse. Even less is known about who is most susceptible to the effect of temperature. Methods: We obtained daily data on temperature and hospital admissions for any cardiovascular cause from all 14 health regions in Ontario, Canada in 1996-2013. A distributed lag non-linear model with 21 days of lag was applied to estimate the cumulative effect of temperature on selected cardiovascular conditions, controlling for air pollutants, influenza activity, relative humidity, long-term trends and day of the week. We estimated risks for cold and heat, defined as temperatures below and above the optimal temperature, which corresponded to the point with minimum cardiovascular morbidity, for each health region, and then pooled across Ontario. To identify potentially vulnerable subpopulations, we conducted stratified analyses by selected comorbidities and demographic characteristics. Results: Between 1996 and 2013, we identified 1.4 million hospitalizations from ischemic heart disease, 443,447 from myocardial infarction, 355,837 from stroke, and 237,979 from ischemic stroke across Ontario. The relationship of temperature and the selected cardiovascular conditions exhibited a U shape, with the minimum risk at ~60th percentile (12oC). For cold (at the 1st percentile temperature), the adjusted rate ratio was 1.12 (95% confidence interval (CI)=1.05-1.19) for ischemic heart disease, 1.28 (95%CI=1.13-1.44) for myocardial infarction, 1.15 (95%CI=1.02-1.30) for stroke, and 1.19 (95%CI=1.03-1.36) for ischemic stroke. Heat also exhibited elevated risks, albeit not significant, for these conditions. We found that the risk of ischemic heart disease from cold was highest among those with a history of conduction disorders, whereas the risk of stroke from cold was highest in those with arrhythmias. Conclusions: Acute exposure to cold temperature contributed to excess hospitalizations from cardiovascular disease in Ontario.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".