Frequency and predictors of post-stroke cognitive impairment in Ethiopian stroke survivors: A cross-sectional study
Bibliographic record
Abstract
Abstract Background: Stroke is emerging as a public health threat to sub-Saharan African countries. Even though cognitive impairment is increasingly being recognized as a major cause of disability in stroke survivors there is no data on the burden of stroke related cognitive dysfunction from Ethiopia. In this study, we aimed to assess the frequency and predictors of post-stroke cognitive impairment in Ethiopian stroke survivors.Methods: Participants were adult stroke survivors who came for follow-up at least 3 months after the last stoke. Demographic and clinical data were collected using a structured questionnaire. We employed the Montreal Cognitive Assessment Scale-Basic (MOCA-B), modified Rankin Scale (mRS) and Patient Health Questionnaire-9 (PHQ-9) to assess post-stroke cognition, functional recovery, and depression, respectively.Results: Among 67 stroke survivors (mean age: 52.1 ± 12.7 years, females: 40.3%, low or no education: 41.8%, median stroke duration: 3 years), 28 (41.8%) had post-stroke cognitive impairment. Of these, 20 (30%) had mild cognitive impairment and 8 (12%) had post-stroke dementia. On multivariate analysis, increased age [AOR=0.24, 95% CI (0.07,0.83)], lower education [AOR=4.02, 95% CI (1.13,14.32)] and poor function recovery (mRS ³3) [AOR=0.27, 95% CI (0.08-0.81)] were independently associated with post-stroke cognitive impairment.Conclusion:Cognitive impairment is frequent among Ethiopian stroke survivors. We found that increased age, low educational attainment, and poor recovery on physical function were independently associated with cognitive decline. Although causality cannot be inferred, physical rehabilitation and better education might play a significant role in building cognitive resilience among stroke survivors.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".