Cognitive Impairment Among Stroke Survivors at the University of Calabar Teaching Hospitalcalabar Cross River State, Nigeria
Bibliographic record
Abstract
Background: Post-stroke cognitive impairment is very prevalent among stroke survivors but is often missed or underdiagnosed. Previous studies in Nigeria have focused more on post-stroke depression, leading to a shortage of information on cognitive impairment after stroke. Aim: To investigate the occurrence of post-stroke cognitive impairment among stroke survivors at the University of Calabar Teaching Hospital (UCTH), Calabar, Nigeria. Materials and Methods: This cross-sectional study was conducted among stroke survivors at the University of Calabar Teaching Hospital. All the stroke survivors had a CT-scan confirmed stroke. A consecutive sampling method was used to recruit respondents. The following questionnaires were administered to 122 stroke survivors. Sociodemographic/clinical questionnaire, National Institute of Health Stroke Scale (NIHSS), The Mini-International Neuropsychiatry Interview (MINI)-Depression module, Mini-Mental State Examination (MMSE), Oslo Social Support Scale (OSSS-3), and the Modified Rankin Scale (MRS). The data were analysed using SPSS version 25. Results: The study recruited 122 stroke survivors. The mean age of the participants was 60.23 ± 13.0. The proportion of male respondents was 62.3%, while that of female respondents was 37.7%. Respondents with the right hemispheric stroke were 51.6%, while those with the left hemispheric stroke were 36.1%. The proportion of respondents with ischaemic stroke (87.1%) was higher than that of those with hemorrhagic stroke (12.3%). The prevalence of post-stroke cognitive impairment among the respondents was 41.8%. Respondents with left hemispheric stroke were more likely to have cognitive impairment than those with right cerebral stroke. Also, respondents with severe stroke-related disability were more likely to have cognitive impairment than those with mild stroke-related disability. However, respondents with a tertiary and secondary level of education were less likely to have cognitive impairment than those with a primary level of education. Conclusion: The result of the study showed that post-stroke cognitive impairment is high among the study population. The condition is often neglected by the primary physicians managing these patients, leading to poor treatment outcomes. Efforts should be made to include cognitive assessment/ treatment in the routine care of stroke patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".