Global cognition is related to upper-extremity motor skill retention in individuals with mild-to-moderate Parkinson disease
Bibliographic record
Abstract
ABSTRACT Background and Purpose Cognitive impairment has been linked to poor motor learning and rehabilitation outcomes in older adult and stroke populations, but this remains unexplored in individuals with Parkinson disease (PD). The purpose of this secondary data analysis from a recent clinical trial ( NCT02600858 ) was to determine if global cognition was related to nine-day skill retention after upper-extremity motor training in individuals with PD. Methods Twenty-three participants with idiopathic PD completed three consecutive days of training on an upper-extremity task. For the purposes of the original clinical trial, participants trained either “on” or “off” their dopamine replacement medication. Baseline, training, and shorter-term (48-hour) retention data have been previously published. Global cognition was evaluated using the Montreal Cognitive Assessment (MoCA). Participant age, baseline performance, MoCA score, and group (medication “on”/”off”) were included in a multivariate linear regression model to predict longer-term (nine day) follow-up performance. Baseline and follow-up performance were assessed for all participants while “on” their medication. Results MoCA score was positively related to follow-up performance, such that individuals with better cognition performed better than those with poorer cognition. Participant age, baseline performance, and medication status were unrelated to follow-up performance. Discussion and Conclusions Results of this secondary analysis align with previous work that suggest cognitive impairment may interfere with motor learning in PD, and that assessing cognition could provide prognostic information about an individual’s responsiveness to motor rehabilitation for a number of clinical populations.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".