Prevalence of Post-stroke Cognitive Impairment and Dementia in a Sudanese Cohort: A Single-center Retrospective Study
Bibliographic record
Abstract
Background: Post-stroke cognitive impairment (PSCI) and dementia are major contributors to disability worldwide. However, data from low- and middle-income countries (LMICs), including Sudan, remain limited. This study aimed to determine the prevalence of PSCI and dementia among Sudanese stroke survivors and identify associated risk factors. Methods: A hospital-based study was conducted at Al-Nou Hospital in Omdurman, Sudan. We adopted a cross-sectional design rather than a purely retrospective one. Eighty-one patients with a prior stroke diagnosis were recruited via purposive sampling (initially targeting 93 based on estimated stroke mortality but adjusted due to local constraints). A structured questionnaire and review of hospital records were used to collect demographic and clinical data; the Montreal Cognitive Assessment (MoCA) was used for cognitive evaluation. Descriptive statistics were obtained for all variables. Bivariate analyses (chi-square or Fisher-Freeman-Halton exact tests, depending on assumptions) and ordinal logistic regression were used to examine associations with four-level MoCA outcomes. Results: Of 81 stroke survivors (mean age 61.9±13.9 years), 72.8% had ischemic stroke, and 27.2% had hemorrhagic stroke. Hypertension (61.2%) and diabetes mellitus (36.2%) were the most common comorbidities. Right-hemisphere strokes were more frequent (64.2%) than left-hemisphere events (35.8%). In a four-level MoCA analysis, sex (female) and higher education were significantly associated with better cognitive outcomes (p<0.05). Comorbidity categories approached significance (p=0.075; exact p=0.063). Ordinal logistic regression confirmed higher education as an independent predictor of improved cognitive status (adjusted OR=4.07, p=0.018). Conclusion: PSCI was highly prevalent in this Sudanese stroke cohort, underscoring the need for systematic cognitive screening and aggressive management of vascular risk factors, particularly hypertension. Higher education and female sex were associated with better cognitive outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".