Motor Skill Retention Impairments in Parkinson’s Disease: A Systematic Review with Meta-analysis
Bibliographic record
Abstract
Abstract The ability to acquire and retain motor skills is essential for persons with Parkinson’s Disease (PD), who usually experience a progressive loss of mobility during the disease. Deficits in the rate of motor skill acquisition have been previously reported in these patients. Whether motor skill retention is also impaired is currently not known. We conducted a review that included 46 studies to determine whether, compared with neurologically intact individuals, motor skill retention is impaired in PD. Meta-analyses revealed that, following a single practice session, persons with PD have deficits in skill retention (SMD = −0.17; 95% CI = −0.32, −0.02; p = 0.0225). However, these deficits are task-specific, affecting sensory motor (SMD = −0.31; 95% CI −0.47, −0.15; p = 0.0002) and visuomotor adaptation (SMD = − 1.55; 95% CI = −2.32, −0.79; p = 0.0001) tasks, but not sequential fine motor (SMD = 0.17; 95% CI = −0.05, 0.39; p = 0.1292) and gross motor tasks (SMD = 0.04; 95% CI = −0.25, 0.33; p = 0.7771). Importantly, retention deficits became non-significant when augmented feedback during practice was provided. Similarly, additional sessions of motor practice restored the deficits observed in sensory motor tasks. Meta-regression analyses confirmed that retention deficits were independent of performance during motor skill acquisition, as well as the duration and severity of the disease. These results are in line with prominent neurodegenerative models of PD progression and emphasize the importance of developing targeted interventions to enhance motor memory processes supporting the retention of motor skills in people with PD.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.024 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".