The Association Between Clinical Characteristics and Motor Symptom Laterality in Patients With Parkinson's Disease
Bibliographic record
Abstract
Background and Purpose: The unilateral onset and persistent asymmetry of motor symptoms are important characteristics of Parkinson's disease (PD). By using scales and wearable sensors, this study explored whether motor symptom laterality could affect non-motor symptom and gait performance. Methods: A total of 130 right-handed patients with PD were enrolled in our study and were divided into two groups according to the side of predominant motor symptom presentation by using the Unified Parkinson's Disease Rating Scale part III. We measured the non-motor symptoms with the Non-motor symptoms Scale, sleep quality with the Parkinson's Disease Sleep Scale and Pittsburgh sleep quality index, cognitive function with the Mini-mental State Examination and Montreal Cognitive Assessment, quality of life with the Parkinson's Disease Questionnaire-39, and the severity of anxiety and depression with the Hamilton Anxiety Scale and Hamilton Depression Scale, respectively. All participants underwent the instrumented stand and walk test, and gait data were collected using a set of JiBuEn gait analysis system. Results: We observed that left-dominant symptom PD patients (LPD) were associated with a greater impairment of sleep quality than right-dominant symptom PD patients (RPD). We found no difference between LPD and RPD in terms of gait performance. However, compared with the severe asymmetry RPD patients (RPD-S), severe asymmetry LPD patients (LPD-S) showed a shorter stride length and decreased range of motion of hip joints. Conclusions: In this study, LPD was associated with a more severe sleep-related dysfunction than RPD. In addition, LPD-S exhibited more gait impairments than RPD-S. Considering that motor symptom laterality may affect the non-motor symptom and gait performance, it should be taken into account when evaluating and treating PD patients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".