Investigation of the Impact of Motor, Nonmotor, Cognitive, and Psychometric Features on Freezing of Gait in Parkinson's Disease
Bibliographic record
Abstract
There are still debates regarding the pathophysiology of freezing of gait (FOG) in Parkinson's disease (PD). This study aims to investigate the potential contribution of the nonmotor symptoms in the pathophysiology of FOG. This was a cross-sectional observational cohort study where we enrolled all consecutive PD patients who applied to our movement disorders outpatient clinics at Etlik City Hospital, Ankara, Turkey, between January 2024 and August 2024. We performed comprehensive assessments including scales to evaluate both motor and nonmotor features, psychometric properties, and cognitive characteristics. In the hierarchical regression analyses, we sought to examine the contributory role of the motor, nonmotor, neuropsychological, and cognitive symptom load on FOG one by one. We included 45 PD patients with a mean age of 61.9 ± 8.6. The median disease duration was 5 years (range: 20), the median MDS-UPDRS-3-off score was 33 (range: 20.5). The comparative analyses between patients with (n = 21) and without FOG (n = 24) revealed that the scores regarding the MDS-UPDRS-1 (p = 0.04), MDS-UPDRS-3 (p = 0.00), MDS-UPDRS-3-axial subscore (p = 0.00), NMSS (p = 0.017), SMMSE (p = 0.02), forward counting (p = 0.044), backward counting (p = 0.015), JLO (p = 0.033), HAM-A (p = 0.006), and HDRS (p = 0.006) were all higher in the FOG (+) group. In the hierarchical regression analyses, the MDS-UPDRS-3-off score was the only predictive factor of FOG severity in the model evaluating the motor factors (B = 0.251, p = 0.000) and also the other models which we included the other nonmotor features one by one. Our findings showed that nonmotor symptoms, cognitive functions, and psychometric properties do not provide a contributory effect to the motor severity on the FOG severity.
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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 | 0.002 |
| 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".