Population‐based stroke and dementia incidence trends: Age and sex variations
Bibliographic record
Abstract
INTRODUCTION: We discovered a concomitant decline in stroke and dementia incidence rates at a whole population level in Ontario, Canada. This study explores these trends within demographic subgroups. METHODS: We analyzed administrative data sources using validated algorithms to calculate stroke and dementia incidence rates from 2002 to 2013. RESULTS: For more than 12 years, stroke incidence remained unchanged among those aged 20 to 49 years and decreased for those aged 50 to 64, 65 to 79, and 80+ years by 22.7%, 36.9%, and 37.9%, respectively. Dementia incidence increased by 17.3% and 23.5% in those aged 20 to 49 and 50 to 64 years, respectively, remained unchanged in those aged 65 to 79 years, and decreased by 15.4% in those aged 80+ years. DISCUSSION: The concomitant decline in stroke and dementia incidence rates may depict how successful stroke prevention has targeted shared risk factors of both conditions, especially at advanced ages where such risk factors are highly prevalent. We lend support for the development of an integrated system of stroke and dementia prevention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".