MétaCan
Menu
Back to cohort
Record W6945246258 · doi:10.25384/sage.c.5496147

Risk factors for delayed-onset dementia after stroke or transient ischemic attack—A five-year longitudinal cohort study

2021· other· en· W6945246258 on OpenAlexaboutno aff

Bibliographic record

VenueSage Journals Data · 2021
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaStroke (engine)Proportional hazards modelCohortProspective cohort studyIschemic strokeCohort studyRisk factor

Abstract

fetched live from OpenAlex

BackgroundStroke not only substantially increases the risk of incident dementia early after stroke but also the risk remains elevated years after.AimWe aimed to determine the risk factors of dementia onset more than three to six months after stroke or transient ischemic attack.MethodsThis is a single-center prospective cohort study. We recruited consecutive subjects with stroke/transient ischemic attack without early-onset dementia. We conducted an annual neuropsychological assessment for five years. We investigated the association between baseline demographic, clinical, genetic (APOEɛ4 allele), and radiological factors as well as incident recurrent stroke with delayed-onset dementia using Cox proportional hazards models.ResultsIn total, 1007 patients were recruited, of which 88 with early-onset dementia and 162 who lost to follow-ups were excluded. Forty-nine (6.5%) out of 757 patients have incident delayed-onset dementia. The presence of ≥3 lacunes, history of ischemic heart disease, history of ischemic stroke, and a lower baseline Hong Kong version of the Montreal Cognitive Assessment (MoCA) score were significantly associated with delayed-onset dementia. APOEɛ4 allele, medial temporal lobe atrophy, and recurrent stroke were not predictive.ConclusionThe presence of ≥3 lacunes, history of ischemic heart disease, history of ischemic stroke, and a lower baseline MoCA score are associated with delayed-onset dementia after stroke/transient ischemic attack.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.635
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0030.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1260.001

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 head0.069
GPT teacher head0.352
Teacher spread0.283 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueSage Journals DataFrench-language works237,207