Structured Multidomain Cognitive Rehabilitation for Stroke: Findings From an Observational Cohort Study
Bibliographic record
Abstract
Abstract BackgroundPersons with mild stroke experience minimal functional difficulties; nonetheless, they are at elevated risk for post-stroke cognitive impairment (PSCI) and cognitive decline. AimsWe report outcomes from an observational cohort study of a structured, multidomain intervention for persons with mild strokes. MethodsThe Stroke Memory RehabiliTation (SMaRT) program comprises weekly two-hour group sessions for six weeks on cognitive strategies, lifestyle and relaxation. Participants were recruited from a tertiary hospital between June 2018 to September 2019. They had MRI-confirmed ischemic strokes with mild functional difficulties (modified Rankin Scale score ≤3). Participants underwent assessments and questionnaires at baseline, 1-week post-program, 3 months post-program, and 6 months post-program. Results Participants (N=108, mean age=63.54±9.22, 31.5% female) demonstrated significant improvement in cognition, mood, activities of daily living (ADL) and quality of life (QOL) (ps<0.05) across timepoints. Participants performed significantly better at 6 months post-program compared to baseline on cognitive tests (MoCA 24.47±3.22 vs. 25.80±2.83, Beta=1.26, 95% CI(0.78,1.74)), p<.001; Visual Cognitive Assessment Test (22.74±3.98 vs. 24.76±3.68, Beta=2.04, 95% CI(1.44,2.65), p<.001); 87% of participants maintained or improved in performance. At 6 months post-program, participants reported greater functional independence (Nottingham Extended ADLs Questionnaire Beta=4.20, 95% CI(2.63,5.77), p<.001); fewer depressive symptoms (Geriatric Depression Scale Beta=-1.44, 95% CI(-1.94, -0.94), p<.001); and improved QOL (Dementia-QOL Questionnaire Beta=5.28, 95% CI(1.09, 2.57), p<.001). ConclusionsThe SMaRT program is a cost-effective, scalable, structured program for PSCI. Findings suggest its potential effectiveness in reducing cognitive decline and improving other domains of well-being, with carry-over benefits after 6 months.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".