Using Ecological Whole Body Kinematics to Evaluate Effects of Medication Adjustment in Parkinson Disease
Bibliographic record
Abstract
BACKGROUND: Functional motor impairments including mobility are major reasons for clinical intervention and medication adjustment in symptomatic therapy for Parkinson's disease (PD). Outcome measures used to assess the impact of medication are mostly based on patients' memory or diaries which, considering the gaps between visits, are neither objective nor very reliable. OBJECTIVE: Investigating the feasibility of using movement features extracted from ecological whole-body kinematics recordings to measure the quantitative and qualitative changes in multiple aspects of mobility after medication changes in PD. METHODS: Eleven patients with PD (PwPD) performed mobility tasks in their own home, wearing a full body wireless inertial sensing based motion capture system. Three scripted walking tasks (walking, fast walking, and walk turns) were examined at baseline and two weeks after medication changes. Clinical scales, including investigator-rated clinical global impression of improvement (CGI-I), were collected at both visits. RESULTS: Out of 59 recorded body joint variables, five were identified as pertinent. Changes were represented in vector space as a plot of mean versus peak amplitude. Regression analysis was used to predict clinical improvement or worsening based on these vector features. The predictors were able to explain (>98.5% of variance) patients' clinical global impression of improvement, thus correctly predicting 5 cases of improvement and 2 cases of worsening. CONCLUSIONS: This study provided a method of extracting clinically meaningful reports from ecological kinematic data showing changes after drug adjustments. The results are presented using a novel concept called change space that may be more understandable for clinical staff.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".