Parkinson Disease: The Relationship Between Non-motor Symptoms and Motor Phenotype
Bibliographic record
Abstract
BACKGROUND: Parkinson disease (PD) presents with motor and non-motor symptoms (NMS). The NMS often precede the onset of motor symptoms, but may progress throughout the disease course. Tremor dominant, postural instability gait difficulty (PIGD), and indeterminate phenotypes can be distinguished using Unified PD Rating scales (UPDRS-III). We hypothesized that the PIGD phenotype would be more likely to develop NMS, and that the non-dopamine-responsive axial signs would correlate with NMS severity. METHODS: We conducted a retrospective cross-sectional chart review to assess the relationship between NMS and PD motor phenotypes. PD patients were administered the NMS Questionnaire, the UPDRS-III, and the Mini-Mental State Examination score. The relationship between NMS burden and PD subtypes was examined using linear regression models. The prevalence of each NMS among difference PD motor subtypes was analyzed using chi-square test. RESULTS: PD patients with more advanced disease based on their UPDRS-III had higher NMS Questionnaire scores. The axial component of UPDRS-III correlated with higher NMS. There was no correlation between NMS and tremor scores. There was a significant correlation between PIGD score and higher NMS burden. PIGD group had higher prevalence in most NMS domains when compared with tremor dominant and indeterminate groups independent of disease duration and severity. CONCLUSIONS: NMS profile and severity vary according to motor phenotype. We conclude that in the PD population, patients with a PIGD phenotype who have more axial involvement, associated with advanced disease and poor motor response, have a higher risk for a higher NMS burden.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".