Improvement of Arm Movement Patterns and Endpoint Control Depends on Type of Feedback During Practice in Stroke Survivors
Bibliographic record
Abstract
BACKGROUND: A major challenge in stroke rehabilitation is restoration of arm motor function. Therapy-induced improvements in arm function may occur via restoration of premorbid movement patterns (recovery) or development of compensatory movement strategies. However, it is unclear whether the learning benefits of practice might be enhanced by incorporating different forms of feedback, focusing on movement outcomes or on specific arm movement patterns. OBJECTIVE: To determine if manipulation of attentional focus by providing either knowledge of results (KR) feedback, focusing on movement outcomes, or knowledge of performance (KP) feedback, focusing on arm movement patterns during repetitive practice of a pointing movement, may lead to arm motor recovery. METHODS: Twenty-eight chronic stroke survivors were randomly assigned to 2 groups that practiced 10 sessions of 75 pointing movements. During practice, groups received either 20% KR about movement precision or faded (26.6% average) KP about arm joint movements. A nondisabled control group (n = 5) practiced the same task with KR. RESULTS: Motor patterns recovered only in KP, as evidenced by immediate and long-term increases in joint range, better interjoint coordination in early movement phases, and generalization of gains. Improvements in clinical impairment and function were related to decreases in compensation (trunk rotation) and recovery of interjoint coordination in mid-movement phases. CONCLUSIONS: In stroke survivors, when the learners' attention was directed to the movements themselves (KP), motor improvements reflect recovery compared to when attention was directed toward movement outcomes (KR).
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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 | 0.003 |
| 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) | 0.002 | 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".