Motor learning after stroke: what we’ve learned and what lies ahead
Bibliographic record
Abstract
Each year, there are millions of new stroke survivors globally. As many as 75% of stroke survivors will have impairments in moving their upper limb(s) that compromise daily activities like eating and grooming. Stroke rehabilitation practices often rely on motor learning principles to facilitate the recovery of motor function. Many principles applied in a therapy setting have been derived from studies involving healthy adults, even though stroke-related brain damage may alter these processes. Other studies have focused on the chronic stage of stroke recovery (>6 months post-stroke), despite most rehabilitation taking place in the first weeks and months post-stroke. It is unclear how motor learning and the underlying neuroplastic changes differ from early (acute and subacute) to late (chronic) stages of recovery. An evidence-based understanding of how motor learning is affected early after stroke, and how these changes interact with the course of motor recovery may hold promise for improving rehabilitation outcomes. In this review, we focus on two major categories of motor learning: motor skill learning and sensorimotor adaptation. We provide a comprehensive review of studies of stroke survivors, organized around a conceptual framework that examines how motor learning is impaired after stroke, what clinical and demographic factors interact with motor learning capacity, and where in the brain lesions are associated with poorer outcomes. We highlight key priorities for future research, including early post-stroke assessment of motor learning, large-scale longitudinal studies, standardized designs for comparability between studies and a better understanding of factors like reinforcement and explicit task instruction that may alter learning. We conclude with recommendations to help bridge the gap between motor learning research and stroke rehabilitation practice.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".