Cold Temperature and Risk of Death Due to Stroke
Bibliographic record
Abstract
Objective: Temperature is associated with the risk of death due to myocardial infarction, but the relationship with cerebrovascular events is poorly understood. We evaluated the association between cold temperature and the risk of death due to haemorrhagic or ischemic stroke.Methods: Using data from provincial death registration certificates, we undertook a case-crossover study to investigate the association between cold temperature and death due to stroke during 1981-2013 in Quebec, Canada. We selected deaths between November and April, the coldest months of the year, and paired them to the temperature data. Temperature data were obtained from Environment Canada monitoring stations for each of the 18 health regions in Quebec. We used conditional logistic regression to compute odds ratios (OR) and 95% confidence intervals (CI) for the association between minimum daily temperature and haemorrhagic and ischemic stroke. Models were adjusted for the duration and quantity of snowfall.Results: There were 13,208 deaths due to haemorrhagic stroke, and 16,383 due to ischemic stroke during the study. The risk of death from haemorrhagic stroke was elevated on cold days as well as the following day. Compared with 0°C, a minimum temperature of -30°C was associated with an OR of 1.22 (95% CI 1.08–1.38) for haemorrhagic stroke the day of exposure, and an OR of 1.18 (95% CI 1.04–1.33) the day following exposure. Associations were weaker and not statistically significant for ischemic stroke.Conclusion: Cold temperature was associated with risk of death due to haemorrhagic stroke. In the context of climate change and a greater frequency of extreme winter events, individuals with predisposing risk factors should be aware of the higher risk of death due to haemorrhagic stroke during cold temperature. Environmental alerts targeting populations at risk, including recommendations to minimize exposure during extreme cold, may be merited.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".