Stroke Incidence by Sex Across the Lifespan
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: We evaluated the influence of age on the association between sex and the incidence of stroke or transient ischemic attack (TIA) using a population-based cohort from Ontario, Canada. METHODS: We followed a cohort of adults (≥18 years) without prior stroke from January 1, 2003 (cohort start date) to March 31, 2018, to identify incident events. We calculated hazard ratios (HRs), in women compared to men, of incident stroke or TIA, adjusted for demographics and comorbidities, overall and stratified by stroke type. We calculated piecewise adjusted HRs for each decade of age to evaluate the effect of age on sex differences in stroke incidence. RESULTS: We followed 9.2 million adults for a median of 15 years and observed 280,197 incident stroke or TIA events. Compared with men, women had an overall lower adjusted hazard of stroke or TIA (HR, 0.82 [95% CI, 0.82-0.83]), with similar findings across all stroke types except for subarachnoid hemorrhage (HR, 1.29 [95% CI, 1.24-1.33]). We found a U-shaped association between age and sex differences in the incidence of stroke or TIA: compared with men, the hazard of stroke was higher in women among those aged ≤30 years (HR, 1.26 [95% CI, 1.10-1.45]), lower among those between ages 40 and 80 years (eg, age 50-59, HR, 0.69 [95% CI, 0.68-0.70]), and similar among those aged ≥80 years (HR, 0.99 [95% CI, 0.98-1.01]). CONCLUSIONS: Overall, women have a lower hazard of stroke than men, but this association varies by age and across stroke types. Recognition of age-sex variations in stroke incidence can help guide prevention efforts to reduce stroke incidence in both men and women.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".