Quantitative Motor Assessment In Parkinson's Disease (PD) with the MovAlyzeR® Digital Writing Tablet (P3.072)
Bibliographic record
Abstract
Objectives: 1) Determine what quantitative motor function test variables best distinguish between subjects with and without Parkinsonism. 2) Correlate findings from quantitative motor testing with standard measures of motor function (Unified Parkinson’s Disease Rating Scale-UPDRS-III). Background: Ordinal rating scales that measure motor function in Parkinsonism can be subjective and may be unable to detect subtle abnormalities. Automated quantitative assessments are of interest due to their objectivity and sensitivity. Design/Methods: 50 subjects (26 patients with Parkinsonism and 24 patients with other neurological disorders) were assessed with the UPDRS and MovAlyzeR®, a digital writing tablet able to measure velocity, acceleration and amplitude of upper extremity movements. Subjects were tested under single and dual task conditions with cognitive distraction. Group means were compared using Mann-Whitney U-tests. Relationships between UPDRS-III and quantitative motor function test scores were assessed using partial correlations adjusted for age. Results: Stroke velocity was slower in Parkinsonian subjects under both single and dual task conditions (p=.046 for single task; p=.072 for dual task), controlling for subject age. Similarly, velocity and acceleration during sentence writing were slower in Parkinsonian subjects (p=.014; p=.051, respectively). Total UPDRS motor scores negatively correlated with stroke velocity (single task r=-.278, p=.062; dual task r=-.379, p=.009). Among UPDRS domains, the strongest correlation was with bradykinesia items (single task r=-.297, p=.045; dual task r=-.377, p=.010). There was also a trend towards correlation between Montreal Cognitive Assessment performance and difference between single and dual task velocity for the non-dominant hand (r=.328, p=.089). Conclusions: MovAlyzeR® quantitative testing distinguished between subjects with Parkinsonism and controls, and stroke velocity and acceleration correlated well with UPDRS motor scores, particularly with measures of bradykinesia rather than tremor. Future studies will examine whether quantitative testing can assess Parkinsonism and changes associated with progression or treatment more sensitively than UPDRS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".