The Effect of Cognitive Impairment on the Health-Related Quality of Life Among Stroke Survivors at a Major Referral Hospital in Ghana.
Bibliographic record
Abstract
BACKGROUND: The sequelae of stroke include both physical and cognitive impairment. However, the physical impairment usually takes center-stage during rehabilitation, while cognitive impairment is largely ignored. Cognitive deficit is very common following stroke and its assessment and identification is crucial as it impacts on the outcome and rehabilitation of stroke survivors. Very few studies have explored any relationship between cognitive impairment and quality of life after stroke and these studies have been inconclusive. This study set out to explore any relationship that might exist between post-stroke cognitive impairment and quality of life of the survivors. METHODS: A descriptive cross-sectional study was conducted at the Stroke unit of the Korle Bu teaching hospital during which 110 stroke survivors were recruited. A structured questionnaire was used to obtain the demographic, clinical characteristics and risk factor profile of the study participants. The HRQOLISP-26 questionnaire which is a stroke specific quality of life scale was used to assess the QoL in this study while the Montreal Cognitive Assessment Scale (MoCA) was used to assess cognitive deficit. RESULTS: Mean age of the 110 study participants was 61±17 years. Presence of cognitive impairment after 3 months was associated with lower quality of life (p=0.003) while increasing age and level of education were associated with lower scores on MoCA scale (p=0.019, p=0.048 respectively). CONCLUSION: Cognitive impairment is strongly associated with poor quality of life following stroke, therefore, cognitive rehabilitation (and not just physiotherapy) should be considered as an integral component in the management of 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 | 0.000 | 0.002 |
| 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".