Cognitive Impairment after Stroke: Prevalence and predictors in a Hospital-Based sample from Cameroon
Bibliographic record
Abstract
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.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".