Seasonal Variations in Stroke Occurrence
Bibliographic record
Abstract
BACKGROUND: Understanding seasonal variations in stroke can help stakeholders identify underlying causes in seasonal trends, and tailor resources appropriately to times of highest needs. We sought to evaluate the seasonal occurrence of stroke and its subtypes. METHODS: We conducted a retrospective cohort study using administrative data from January 1st, 2003, to December 31st, 2017, in Ontario, Canada's most populous province. We evaluated seasonal variations in stroke occurrence by subtype, via age/sex standardized rates and adjusted rate ratios using Poisson regressions. In those with stroke, we evaluated 30-day case fatality risks by season, adjusted for age, sex, stroke type, and comorbid conditions, and then used Cox proportional hazard models to estimate the effect of season on the fatality. The administrative data used in this study were from the Canadian Institute for Health Information's Discharge Abstract Database, the National Ambulatory Care Reporting System Database, the Ontario Registered Persons Database, and the 2006 and 2011 Canada Census and linked administrative databases. RESULTS: During our study period, we observed 394,145 strokes or TIA events, with a decrease in monthly hospitalization/emergency department visits per 100,000 people between January 2003 and December 2017 from 24.22 to 17.43. Compared to the summer, overall stroke occurrence was similar in the spring but slightly lower in the fall (adjusted rate ratio [aRR] 0.97, 95% confidence interval [CI] 0.96-0.98) and winter (aRR 0.94, 95% CI: 0.94-0.95). There were minor variations by stroke subtype. Winter was associated with the highest risk of stroke case fatality compared to the summer (12.4% vs. 11.4%, adjusted hazard ratio 1.10, 95% CI: 1.07-1.13). CONCLUSIONS: We found seasonal variations in stroke occurrence and case fatality, although the absolute differences were small. Further work is needed to better understand how environmental or meteorological factors might affect stroke risk.
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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 | 0.002 |
| 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".