Emerging evidence of the association between cognitive deficits and arm motor recovery after stroke: A meta-analysis
Bibliographic record
Abstract
PURPOSE: Motor and cognitive impairments are common and often coexist in patients with stroke. Although evidence is emerging about specific relationships between cognitive deficits and upper-limb motor recovery, the practical implication of these relationships for rehabilitation is unclear. Using a structured review and meta-analyses, we examined the nature and strength of the associations between cognitive deficits and upper-limb motor recovery in studies of patients with stroke. METHODS: Motor recovery was defined using measures of upper limb motor impairment and/or activity limitations. Studies were included if they reported on at least one measure of cognitive function and one measure of upper limb motor impairment or function. RESULTS: Six studies met the selection criteria. There was a moderate association (r = 0.43; confidence interval; CI:0.09- 0.68, p = 0.014) between cognition and overall arm motor recovery. Separate meta-analyses showed a moderately strong association between executive function and motor recovery (r = 0.48; CI:0.26- 0.65; p < 0.001), a weak positive correlation between attention and motor recovery (r = 0.25; CI:0.04- 0.45; p = 0.023), and no correlation between memory and motor recovery (r = 0.42; CI:0.16- 0.79; p = 0.14). CONCLUSION: These results imply that information on the presence of cognitive deficits should be considered while planning interventions for clients in order to design more personalized interventions tailored to the individual for maximizing upper-limb 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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.039 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".