The association of extreme environmental heat with incidence and outcomes of out-of-hospital cardiac arrest in British Columbia: A time series analysis
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Bibliographic record
Abstract
Background: The impact of extreme heat on out-of-hospital cardiac arrest (OHCA) incidence and outcomes is under-studied. We investigated OHCA incidence and outcomes over increasing temperatures. Methods: We included non-traumatic EMS (Emergency Medical Services)-assessed OHCAs in British Columbia during the warm seasons of 2020-2021. We fit a time-series quasi-Poisson generalized linear model to estimate the association between temperature and incidence of both EMS-assessed, EMS-treated, and EMS-untreated OHCAs. Second, we employed a logistic regression model to estimate the association between "heatwave" periods (defined as a daily mean temperature > 99th percentile for ≥ 2 consecutive days, plus 3 lag days) with survival and favourable neurological outcomes (cerebral performance category ≤ 2) at hospital discharge. Results: Of 5478 EMS-assessed OHCAs, 2833 were EMS-treated. OHCA incidence increased with increasing temperatures, especially exceeding a daily mean temperature of 25 °C Compared to the median daily mean temperature (16.9 °C), the risk of EMS-assessed (relative risk [RR] 3.7; 95%CI 3.0-4.6), EMS-treated (RR 2.9; 95%CI 2.2-3.9), and EMS-untreated (RR 4.3; 95%CI 3.2-5.7) OHCA incidence were higher during days with a temperature over the 99th percentile. Of EMS-treated OHCAs, during the heatwave (n = 179) and non-heatwave (n = 2654) periods, 4 (2.2%) and 270 (10%) survived and 4 (2.2%) and 241 (9.2%) had favourable neurological outcomes, respectively. Heatwave period OHCAs had decreased odds of survival (adjusted OR 0.28; 95%CI 0.10-0.79) and favourable neurological outcome (adjusted OR 0.31; 95%CI 0.11-0.89) at hospital discharge, compared to other periods. Conclusion: Extreme heat was associated with a higher incidence of OHCA, and lower odds of survival and favourable neurological status at hospital discharge.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it