Does Provision of Extrinsic Feedback Result in Improved Motor Learning in the Upper Limb Poststroke? A Systematic Review of the Evidence
Bibliographic record
Abstract
BACKGROUND: Recovery of the upper limb (UL) after a stroke occurs well into the chronic stage. Stroke survivors can benefit from adaptive plasticity to improve UL movement through motor relearning. The provision of feedback has been shown to decrease the use of compensatory UL movement patterns. However, the effectiveness of feedback in improving UL motor recovery after a stroke has not yet been systematically reviewed. OBJECTIVE: The objective of this review was to systematically examine the role of extrinsic feedback on implicit motor learning after stroke, focusing on UL movement and functional recovery. RESULTS: The authors retrieved 9 studies that examined the role of feedback on UL motor recovery. Of these, 6 were randomized controlled trials (RCTs), 1 was a single-subject design, 1 was a pre-post design, and 1 was a cohort study. The studies were rated on the basis of Sackett's levels of evidence and PEDro (Physiotherapy Evidence Database) scores for RCTs. Levels of evidence were limited (level 2b) for UL motor learning of the less-affected extremity and strong (level 1a) for the more-affected extremity. DISCUSSION AND CONCLUSIONS: The results suggest that people with stroke may be capable of using extrinsic feedback for implicit motor learning and improving UL motor recovery. Emergent questions regarding the advantages of using different media for feedback delivery and the optimal type and schedule of feedback to enhance motor learning in patient populations still need to be addressed.
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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 | 0.009 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".