Prevalence and predictors of post-stroke cognitive impairment among stroke survivors in Uganda
Bibliographic record
Abstract
BACKGROUND: Little is known about the characteristics and determinants of post-stroke cognitive impairment in residents of low- and middle-income countries. The objective of this study was to determine the frequencies, patterns, and risk factors for cognitive impairment in a cross-sectional study of consecutive stroke patients cared for at Uganda's Mulago Hospital, located in sub-Saharan Africa. METHODS: 131 patients were enrolled a minimum of 3-months after hospital admission for stroke. A questionnaire, clinical examination findings, and laboratory test results were used to collect demographic information and data on vascular risk factors and clinical characteristics. Independent predictor variables associated with cognitive impairment were ascertained. Stroke impairments, disability, and handicap were assessed using the National Institute of Health Stroke Scale (NIHSS), Barthel Index (BI), and modified Rankin scale (mRS), respectively. The Montreal Cognitive Assessment (MoCA) was used to assess participants' cognitive function. Stepwise multiple logistic regression was used to identify variables independently associated with cognitive impairment. RESULTS: The overall mean MoCA score was 11.7-points (range 0.0-28.0-points) for 128 patients with available data of whom 66.4% were categorized as cognitively impaired (MoCA < 19-points). Increasing age (OR 1.04, 95% CI 1.00-1.07; p = 0.026), low level of education (OR 3.23, 95% CI 1.25-8.33; p = 0.016), functional handicap (mRS 3-5; OR 1.84, 95% CI 1.28-2.63; p < 0.001) and high LDL cholesterol (OR 2.74, 95% CI 1.14-6.56; p = 0.024) were independently associated with cognitive impairment. CONCLUSIONS: Our findings highlight the high burden and need for awareness of cognitive impairment in post stroke populations in the sub-Saharan region and serve to emphasize the importance of detailed cognitive assessment as part of routine clinical evaluation of patients who have had a stroke.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".