Feedback and Cognition in Arm Motor Skill Reacquisition After Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: A debated subject in stroke rehabilitation relates to the best type of training approach for motor recovery. First, we analyzed the effects of repetitive movement practice in 2 feedback conditions (knowledge of results [KR]; knowledge of performance, [KP]) on reacquisition of reaching. Second, we evaluated the impact of cognitive impairment on motor relearning ability. METHODS: A randomized controlled clinical trial was conducted in Montreal-area rehabilitation centers between 1998 and 2003 with 37 patients with chronic hemiparesis. Patients were randomly assigned to 3 groups: (1) KR (n=14) practiced a reaching task involving 75 repetitions per day, 5 days per week for 2 weeks, with 20% KR about movement precision; (2) KP (n=14) trained on the same task and schedule as KR but with faded KP about joint motions; and (3) control (C; n=9) practiced a nonreaching task. Physical (motor impairment, function) and kinematic (movement time, precision, segmentation, variability) variables were assessed before and after (immediately, 1 month) practice. Cognitive functions (memory, attention, mental flexibility, planning) were also evaluated. RESULTS: Kinematic gains in KR (precision) and KP (time, variability) exceeded those in C and depended on memory and mental flexibility deficits. In KP, more severely impaired patients made the most clinical gains (>2xC), which were related to memory and planning abilities. CONCLUSIONS: Use of KP during repetitive movement practice resulted in better motor outcomes. Stroke severity together with cognitive impairments are important factors for choosing motor rehabilitation interventions after stroke.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".