Effects of Robot-Assisted Upper Extremity Training on Cognitive and Physical Functions in Patients With Stroke
Bibliographic record
Abstract
OBJECTIVE: Primary aim was to investigate the effects of upper extremity robot-assisted training, applied in addition to conventional rehabilitation program, on cognitive functions after stroke. Secondary aim was to investigate its effects on upper extremity motor functions and activities, hand dexterity, and daily living activities. DESIGN: Forty postacute stroke patients were randomized into robotic ( n = 20) and control ( n = 20) groups. All patients received a conventional rehabilitation program for 6 weeks, total 30 sessions. The robotic group received additional robot-assisted training to the affected upper extremity by exoskeleton robotic system at each session. Cognitive functions (Montreal Cognitive Assessment), upper extremity motor functions (Fugl-Meyer Assessment of Upper Extremity), upper extremity activities (Motor Activity Log-28), hand dexterity (Box and Block Test), and daily living activities (Functional Independence Measure) were assessed before and after the treatment, and at 3-mo follow-up. RESULTS: Both groups showed significant improvements regarding primary and secondary outcomes ( P < 0.05). However, improvements in all outcome measures did not differ significantly between the groups ( P > 0.0167). CONCLUSIONS: Upper extremity robot-assisted training applied in addition to conventional rehabilitation program in postacute stroke provided no extra benefit in terms of improvements in cognitive functions, as well as upper extremity motor functions and activities, hand dexterity, and daily living activities.
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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 | 0.000 |
| 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".