Relationships Between Cognitive Impairments and Motor Learning After Stroke: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Stroke is one of the leading causes of chronic disability worldwide. Sensorimotor recovery relies on principles of motor learning for the improvement of movement and sensorimotor function after stroke. Motor learning engages several cognitive processes to effectively learn and retain new motor skills. However, cognitive impairments are common and often coexist with motor impairments after stroke. The specific relationships between poststroke cognitive impairments and motor learning have not been determined. OBJECTIVES: To summarize the existing evidence related to cognitive impairments and motor learning after stroke. Specific goals were to determine: (1) how motor learning is studied in individuals with poststroke cognitive impairments; (2) how cognitive impairments are assessed; (3) which cognitive domains impact motor learning. RESULTS: Over 400 studies were screened for specific inclusion criteria and 19 studies that related poststroke cognitive impairments to motor learning were included. Studies used a wide variety of experimental designs, sample sizes, and measures for cognitive evaluation. Cognitive impairments impacting motor improvement and learning capacity after stroke were reported in all but 4 studies. The most common domains impacting motor learning were attention, executive function, and memory. CONCLUSION: Detailed cognitive assessments, retention testing, and a combination of clinical and kinematic outcomes are recommended for future studies. The presence of specific cognitive impairments measured with sensitive instruments should be considered when designing effective training interventions for patients with stroke to maximize sensorimotor recovery.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.012 |
| 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".