The effects of music‐supported therapy on motor, cognitive, and psychosocial functions in chronic stroke
Bibliographic record
Abstract
Neuroplasticity accompanying learning is a key mediator of stroke rehabilitation. Training in playing music in healthy populations and patients with movement disorders requires resources within motor, sensory, cognitive, and affective systems, and coordination among these systems. We investigated effects of music-supported therapy (MST) in chronic stroke on motor, cognitive, and psychosocial functions compared to conventional physical training (GRASP). Twenty-eight adults with unilateral arm and hand impairment were randomly assigned to MST (n = 14) and GRASP (n = 14) and received 30 h of training over a 10-week period. The assessment was conducted at four time points: before intervention, after 5 weeks, after 10 weeks, and 3 months after training completion. As for two of our three primary outcome measures concerning motor function, all patients slightly improved in Chedoke-McMaster Stroke Assessment hand score, while the time to complete Action Research Arm Test became shorter in the MST group. The third primary outcome measure for well-being, Stroke Impact Scale, was improved for emotion and social communication earlier in MST and coincided with the improved executive function for task switching and music rhythm perception. The results confirmed previous findings and expanded the potential usage of MST for enhancing quality of life in community-dwelling chronic-stage 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 | 0.001 |
| 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) | 0.001 | 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".