Neuroepidemiology of Post‐Stroke Dementia in a Sub‐Saharan African Population
Bibliographic record
Abstract
BACKGROUND: The neuroepidemiological and clinical characteristics of post-stroke dementia (PSD) in Sub-Saharan African populations have not been sufficiently described. The objective of this study was to determine the prevalence, risk factors, and clinical presentation of PSD in a sub-Saharan African population of stroke survivors. METHOD: Cognitive impairment was assessed using the Montreal Cognitive Assessment (MoCA), Dubois' five-word test, and the frontal assessment battery. Post-stroke dementia was diagnosed using the National Institute of Neurological Disorders and Stroke-Association Internationale pour la Recherche et Enseignement en Neurosciences (NINDS-AIREN) criteria. The clinical dementia rating scale (CDR) was used to assess and stage the severity of PSD. RESULT: A total of 412 stroke survivors were screened, of whom 78 met the criteria for PSD with an age range between 38 and 84, giving a prevalence of 18.9%. Factors associated with PSD included silent brain infarctions, markers of small vessel disease, atrial fibrillation, age, and level of education. Multi-infarct PSD was the most common type. The most affected cognitive domain was executive functions, and most (74.9%) PSD patients had severe PSD based on CDR. None of the patients was receiving dementia-specific care at the time of diagnosis. CONCLUSION: PSD is a growing public health problem in Sub-Saharan African populations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".