Abstract 67: Risk and Time-Course of Post-Stroke Dementia: A Population-Wide Cohort Study, 2002-2022
Bibliographic record
Abstract
Background: People with acute stroke are at high risk of dementia. Population-wide data on the risk and time-course of dementia after stroke are lacking. Methods: We conducted a population-wide analysis of over 15 million people in Ontario, Canada between 2002-2022. Using linked administrative databases, we compared all 90-day survivors of first acute ischemic stroke or intracerebral hemorrhage (ICH) to controls in the general population and with acute myocardial infarction (AMI) by matching 1:1 on age, sex, rural residence, neighborhood marginalization, and vascular comorbidities, and excluded people with prior dementia. We evaluated the rate of dementia per 100 person-years using a validated definition which included hospitalization, physician claims, and medications, and calculated cause-specific hazard ratios overall and across follow-up time. Results: We identified 180,940 people with acute stroke, of which 33,765 (18.7%) developed dementia over mean follow-up of 5.5 years (max 20y). The rate of dementia per 100 person-years was highest after acute stroke compared to the general population (3.40 vs. 1.88) and compared to AMI (3.23 vs. 1.81). The overall risk of dementia was higher in those with acute stroke compared to the general population (HR 1.79, 95% CI 1.76-1.82; Figure 1A), and particularly after ICH (HR 2.43, 2.31-2.57). The results were similar when compared to AMI (HR 1.77, 1.74-1.80; Figure 1C). There was an almost 3-fold elevation of dementia risk in the first year after stroke, decreasing to 1.5-fold by 5 years and remaining elevated even 20 years after (Figure 1B/1D). Conclusion: In this large, population-wide study almost one fifth of people developed dementia after stroke, with an 80% higher risk of developing dementia compared to matched controls. The risk was highest early after stroke but remained elevated 20 years after, raising the urgency of developing interventions for dementia prevention in acute and chronic survivors of stroke.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".