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Record W4409837379 · doi:10.1002/gps.70087

Incidence and Factors Associated With Cognitive Impairment 90 Days After First Ever Ischemic Stroke

2025· article· en· W4409837379 on OpenAlexaboutno aff
Małgorzata Dec‐Ćwiek, Paweł Wrona, Tomasz Homa, Joanna Słowik, Aleksandra Bodzioch, Agnieszka Słowik

Bibliographic record

VenueInternational Journal of Geriatric Psychiatry · 2025
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
FundersUniwersytet Jagielloński Collegium Medicum
KeywordsMontreal Cognitive AssessmentMedicineStroke (engine)Logistic regressionIncidence (geometry)CognitionInternal medicineAnxietyCognitive impairmentPhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVES: Post-stroke cognitive impairment (PSCI) is prevalent among stroke survivors, negatively impacting long-term outcomes. We aimed to assess the prevalence of PSCI and its risk factors in participants from the iBioStroke study (n = 1042), 90 days after their first ischemic stroke. METHODS: We prospectively analyzed data from 582 participants, without cognitive problems before stroke based on the structured interview with the patient, a family member or a caregiver, and/or clinical documentation (if available), who completed the Montreal Cognitive Assessment (MoCA) at discharge and 90 days post-stroke. Two MoCA cut-offs were used to define PSCI: a score of ≤ 25 in the first model and ≤ 22 in the second model. Multivariate logistic regression was employed to identify independent risk factors for PSCI based on 30 collected parameters. RESULTS: In the first model, PSCI was identified in 418 (71.8%) participants at day 90. Independent risk factors included older age (OR = 1.05; 95% CI:1.02-1.08), fewer years of education (OR = 0.83; 95% CI: 0.73-0.93), lower MoCA scores at discharge (OR = 0.76; 95% CI: 0.69-0.84), higher anxiety levels (HADS-A) at day 90 (OR = 1.10; 95% CI: 1.01-1.21), and larger stroke volume (OR = 1.01; 95% CI: 1.00-1.01). In the second model, PSCI was observed in 294 (50.5%) participants. Older age (OR = 1.06; 95% CI: 1.03-1.09), fewer years of education (OR = 0.87; 95% CI: 0.78-0.96), lower MoCA scores at discharge (OR = 0.83; 95% CI: 0.77-0.88), and higher depression levels (HADS-D) at day 90 (OR = 1.10; 95% CI: 1.03-1.18) were significant predictors. CONCLUSIONS: Based on our data, PSCI seems to be a common consequence of stroke. Both irreversible factors, such as age and educational level, stroke volume, and potentially modifiable factors, including post-stroke anxiety or depression and acute cognitive impairment, contribute to PSCI risk. These findings underscore the importance of early cognitive and psychiatric interventions in stroke survivors.

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)0.003
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.006
GPT teacher head0.267
Teacher spread0.261 · 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

Citations6
Published2025
Admission routes1
Has abstractyes

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