Patient Perspectives on Upper‐Limb Daily Function in Parkinson's Disease
Bibliographic record
Abstract
BACKGROUND: Dexterity impairments are common among people with Parkinson's disease (PWP), yet little is understood about the effect of upper-limb (UL) dysfunction on daily activity performance. OBJECTIVES: The aims were to (1) map the dexterity activities most affected and meaningful to PWP; (2) explore the associations between perceived dexterity function and disease severity, cognitive and motor UL impairments, dexterity ability, self-reported activities of daily living (ADL) function, and quality of life (QOL); (3) investigate variables explaining perceived dexterity function; and (4) examine the differences in perceived dexterity function based on dominance affectedness. METHODS: A total of 43 PWP (mean age = 70.00 years, standard deviation [SD] = 6.75) were assessed for perceived dexterity function (36-item Dexterity Questionnaire [DextQ-36]), dexterity ability (Coin Rotation Task), disease severity (modified Hoen and Yahr Scale), self-reported ADL function and motor UL impairments (Movement Disorder Society-Unified Parkinson's Disease Rating Scale), cognition (Montreal Cognitive Assessment), and QOL (Parkinson's Disease Questionnaire-39). RESULTS: The leading dexterity activities participants reported as difficult and meaningful included using a touchscreen, pulling on socks, and dialing a phone. Perceived dexterity significantly correlated with self-reported ADL function (r = 0.716), QOL (r = 0.691), disease severity (r = 0.470), and dominant-hand dexterity (r = 0.432). Dexterity ability and disease severity explained 30% of perceived dexterity variance. No differences in perceived dexterity function based on dominance affectedness were found. CONCLUSIONS: PWP encounter challenges in complex dexterity tasks that impact their independence. Before interventions focused on UL function are initiated, assessments of PWP should include inquiries about the meaningfulness of challenging dexterity activities.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".