Abstract TMP54: Seasonal Patterns of Ischemic Stroke in the United States
Bibliographic record
Abstract
Background: Seasonal variation in stroke occurrence and outcomes has been reported in small populational studies but the significance of a seasonal effect is uncertain. The important patient and climate variables underlying this phenomenon have not yet been identified. Methods: Using Get With The Guidelines-Stroke data from 2011 to 2015, we studied 457,638 consecutive ischemic stroke patients at 896 sites. We calculated the relative prevalence of acute stroke admissions in each season. Baseline characteristics and discharge outcomes were compared across seasons and the effects of season, climate region (as defined by the National Climatic Data Center), and climate variables on outcomes were analyzed using logistic regression. In secondary analyses, odds ratios were calculated for various outcomes. Results: Among all cases of ischemic stroke, there was significant difference in the frequency distributions of cases across seasons (p<.0001), with the highest percentage of strokes occurring in winter (25.5%). Across seasons, winter patients had the highest percentage of atrial fibrillation (17.7%), longest median time to arrival (202 minutes), highest in-hospital mortality (4.4%), and most discharge mRS scores > 3 (43.7%). P<.0001 for all the above. Higher precipitation and temperature were associated with reduced mortality independent of climate region or season (OR .93, CI .89-.97, p=.0005 and OR .97, CI .97-.98, p<.0001, respectively). Odds ratios for in-hospital mortality are summarized in the accompanying table. Conclusions: More patients presented with acute ischemic stroke in winter relative to other seasons, accompanied by trends toward poorer outcomes. Although an association with mortality was seen for winter in our unadjusted analysis, confounding variables likely exist. Precipitation and temperature were independently associated with stroke mortality.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".