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Record W4406995402 · doi:10.1161/str.56.suppl_1.dp13

Abstract DP13: Long-term temporal trends in post-stroke dementia, 2002-2022: A population-wide cohort study

2025· article· en· W4406995402 on OpenAlexaffabout
Raed A. Joundi, Jiming Fang, Peter C. Austin, Eric E. Smith, Amy Yu, Vladimir Hachinski, Luciano A. Sposato, Aravind Ganesh, Mukul Sharma, Moira K. Kapral

Bibliographic record

VenueStroke · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsUniversity of CalgaryWestern UniversityInstitute for Clinical Evaluative SciencesUniversity of TorontoMcMaster University
Fundersnot available
KeywordsMedicineDementiaStroke (engine)CohortTerm (time)Cohort studyPopulationPediatricsGerontologyInternal medicineDiseaseEnvironmental health

Abstract

fetched live from OpenAlex

Background: People with stroke are at high risk of dementia. There have been reductions in stroke case fatality and disability but temporal trends in the incidence and absolute burden of post-stroke dementia have not been described. Methods: We did a population-wide analysis of over 15 million people in Ontario, Canada between 2002-2022. Using linked administrative databases, we identified all 90-day dementia-free survivors of first acute ischemic stroke or intracerebral hemorrhage (ICH). We evaluated dementia incidence from 90-days after stroke onwards using a validated definition which included hospitalization, physician claims, and dementia medications. We calculated 1-year and 5-year incidence of dementia as percentages and per 100 person-years for each fiscal year, age-/sex-standardized by the 2002 population and with follow-up until March 2022. We stratified incidence trends by sex, stroke type, and severity (90-day home time of <60 days indicating moderate-severe stroke). We described trends in absolute number of people with post-stroke dementia, stratified by sex, and used linear regression to evaluate significance. Results: We identified 175,980 people with acute stroke surviving dementia-free to 90 days. From 2002-2021, there was modestly decreasing 1- and 5-year dementia incidence, primarily occurring from 2011 onwards (Figure 1). 5-year dementia decreased by an absolute change of -2.4% (15.5% to 13.1%) and a relative change of -15%. However, there was an increase in the number of people surviving with stroke (Figure 1A-B), so the absolute number of people with post-stroke dementia remained stable or increased over time (Figure 2). Women had higher age-standardized dementia incidence than men, but no difference in trends (Figure 3A-B). There was decreasing dementia incidence for ischemic stroke but no significant change for ICH (Figure 3C-D). Those with 90-day home time <60 days had twice the incidence of dementia compared to those with > 60 days, with no change over time (Figure 3E-F). Conclusion: In this large, population-wide study from 2002-2022, a modest decrease in post-stroke dementia incidence was offset by increasing numbers of stroke survivors and resulted in a rising absolute burden of post-stroke dementia. Those with more severe stroke had 2-fold higher dementia rate which did not change over time despite improvements in stroke care. New strategies are needed to address the persistent and increasing burden of post-stroke dementia.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.693
Threshold uncertainty score0.617

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.005
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.021
GPT teacher head0.296
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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