Temperature and Precipitation Associate With Ischemic Stroke Outcomes in the United States
Bibliographic record
Abstract
Background There is disagreement in the literature about the relationship between strokes and seasonal conditions. We sought to (1) describe seasonal patterns of stroke in the United States, and (2) determine the relationship between weather variables and stroke outcomes. Methods and Results We performed a cross-sectional study using Get With The Guidelines-Stroke data from 896 hospitals across the continental United States. We examined effects of season, climate region, and climate variables on stroke outcomes. We identified 457 638 patients admitted from 2011 to 2015 with ischemic stroke. There was a higher frequency of admissions in winter (116 862 in winter versus 113 689 in spring, 113 569 in summer, and 113 518 in fall; P<0.0001). Winter was associated with higher odds of in-hospital mortality (odds ratio [OR] 1.08 relative to spring, confidence interval [ CI ] 1.04-1.13, P=0.0004) and lower odds of discharge home ( OR 0.92, CI 0.91-0.94, P<0.0001) or independent ambulation at discharge ( OR 0.96, CI 0.94-0.98, P=0.0006). These differences were attenuated after adjusting for climate region and case mix and became inconsistent after controlling for weather variables. Temperature and precipitation were independently associated with outcome after multivariable analysis, with increases in temperature and precipitation associated with lower odds of mortality ( OR 0.95, CI 0.93-0.97, P<0.0001 and OR 0.95, CI 0.90-1.00, P=0.035, respectively). Conclusions Admissions for ischemic stroke were more frequent in the winter. Warmer and wetter weather conditions were independently associated with better outcomes. Further studies should aim to identify sensitive populations and inform public health measures aimed at resource allocation, readiness, and adaptive strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".