Upper-Limb Assessment in People with Parkinson Disease: Is It a Priority for Therapists, and Which Assessment Tools Are Used?
Bibliographic record
Abstract
PURPOSE: To investigate the frequency of physiotherapy and occupational therapy assessment of the upper limb (UL) in people with Parkinson disease (PD) and to identify the impairments and activity limitations assessed and the methods used. METHOD: A custom-designed questionnaire was used to survey physiotherapists and occupational therapists with previous experience in managing people with PD, using targeted recruitment to invite physiotherapy conference attendees, clinicians employed in movement disorders programmes, and practitioners in neurology and gerontology to respond either on paper or online. RESULTS: Of the 190 respondents (122 physiotherapists, 68 occupational therapists), 54% reported consistently assessing the UL. A majority (>60%) assessed impairments specific to PD, but few quantified these using standardized measures. Activity limitations, largely relating to manual dexterity, were assessed using observational analysis (61%), non-standardized timed activities (46%), and standardized outcome measures (61%), most generic or developed for evaluating other neurological conditions. More than 10% of respondents could not identify an appropriate standardized measure. CONCLUSIONS: Slightly more than half of respondents regularly assessed the UL. Respondents reported widespread use of non-standardized methods to assess PD-specific impairments. Standardized measures were more frequently used to evaluate activity limitations, but despite the unique movement disorders associated with PD, the clinimetric properties of most of the tools identified have not been established in this population. Education and further clinimetric investigation of measures in use are needed to facilitate evidence-based practice in this area.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".