Quantitative mobility measures complement the MDS-UPDRS for characterization of Parkinson’s disease heterogeneity
Bibliographic record
Abstract
Abstract Introduction Emerging technologies show promise for enhanced characterization of Parkinson’s Disease (PD) motor manifestations. We evaluated quantitative mobility measures from a wearable device compared to the conventional motor assessment, the Movement Disorders Society-Unified PD Rating Scale part III (motor MDS-UPDRS). Methods We evaluated 176 subjects with PD (mean age 65, 65% male, 66% H&Y stage 2) at the time of routine clinic visits using the motor MDS-UPDRS and a structured 10-minute motor protocol, which included a 32-ft walk, Timed Up and Go (TUG), and standing posture with eyes closed, while wearing a body-fixed sensor (DynaPort MT, McRoberts BV). Regression models examined 12 quantitative mobility measures for associations with (i) motor MDS-UPDRS, (ii) motor subtype (tremor dominant vs. postural instability/gait difficulty), (iii) Montreal Cognitive Assessment (MoCA), and (iv) physical functioning disability (PROMIS-29). All analyses included age, gender, and disease duration as covariates. Models iii-iv were secondarily adjusted for motor MDS-UPDRS. Results Quantitative mobility measures from gait, TUG transitions, turning, and posture were significantly associated with motor MDS-UPDRS (7 of 12 measures, p< 0.05) and subtype (6 of 12 measures, p< 0.05). Compared with motor MDS-UPDRS, several quantitative mobility measures accounted for ∼1.5-fold increased variance in either cognition or physical functioning disability. Among minimally-impaired subjects within the bottom quartile of motor MDS-UPDRS, including subjects with normal gait exam, the measures captured substantial residual motor heterogeneity. Conclusion Clinic-based quantitative mobility assessments using a wearable sensor captured features of motor performance beyond those obtained with the motor MDS-UPDRS and may offer enhanced characterization of disease heterogeneity.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".