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Cognitive Impairment after Stroke: Prevalence and predictors in a Hospital-Based sample from Cameroon

2025· article· W4415369559 on OpenAlexaboutno aff
Annick Mélanie Magnerou, MS MS Ndom-Ebongue, V Sini, Daniel Gams Massi, Yacouba Njankouo Mapoure, Callixte Kuate Tegueu, Jacques Doumbé

Bibliographic record

VenueJournal of African clinical cases and reviews. · 2025
Typearticle
Language
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
Fundersnot available
KeywordsNeurocognitiveStroke (engine)CognitionAffect (linguistics)Cognitive impairmentNeurologyQuality of life (healthcare)Multivariate analysis

Abstract

fetched live from OpenAlex

Background: Stroke is a major cause of long-term disability, often accompanied by neurocognitive and executive function impairments. These sequelae significantly affect patients' quality of life, functional independence, and social reintegration. Despite their importance, little data is available on post-stroke cognitive impairment and its predictors in sub-Saharan Africa. Objective: To determine the prevalence and predictors of neurocognitive disorders in the post-acute phase of stroke. Methodology: We conducted a cross-sectional case control study study over nine months among stroke survivors (stroke+) attending outpatient neurology consultations at Laquintinie Hospital in Douala, Cameroon. These patients were matched for age, sex, and comorbidities with controls who had never experienced a stroke (stroke−). Participants with a history of psychiatric illness or prior cognitive impairment were excluded. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA). Data were analyzed using SPSS v20.0. Results: A total of 223 participants were included (122 stroke+ and 101 stroke−). The mean age was 58.1 ± 10.4 years in the stroke+ group and 56.5 ± 10.3 years in the stroke− group (p > 0.05), with a male-to-female ratio of 1.1. The prevalence of neurocognitive disorders (NCD) was 28% in stroke+ patients versus 8.5% in controls (p < 0.001). Multivariate analysis identified age > 65 years (p < 0.001), alcohol use (p = 0.001), low educational level (p = 0.001), hemorrhagic stroke (p = 0.003), and NIHSS > 5 (p = 0.002) as independent predictors. Conclusion: Stroke survivors are four times more likely to develop neurocognitive disorders compared to individuals without stroke. One in three stroke patients presents cognitive impairment, and nearly 40% exhibit executive dysfunction. Early identification of predictive factors may help improve rehabilitation strategies and long-term outcomes. Keywords: stroke, neurocognitive disorders, predictors, sub-Saharan Africa.

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 score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.353
Teacher spread0.324 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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