Nonmotor Features in Parkinson’s Disease: What Are the Most Important Associated Factors?
Bibliographic record
Abstract
Introduction. The purpose of this study was to demonstrate the frequency and severity of nonmotor symptoms and their correlations with a wide range of demographic and clinical factors in a large cohort of patients with Parkinson's disease (PD). Methods. 268 PD patients were assessed using the validated Movement Disorders Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS), the Beck Depression Inventory (BDI), Parkinson's Disease Questionnaire (PDQ-39), the Hoehn and Yahr scale (HY), the Schwab and England Activities of Daily Living (SE-ADL) Scale, and the Minimental State Examination (MMSE). Results. Nonmotor symptoms had a strong positive relationship with depression and lower quality of life. Also, age, duration and severity of PD, cognitive impairment, daily dose, and duration of levodopa treatment correlated with the burden of nonmotor symptoms. Patients with postural instability and gait disorder (PIGD) dominance or with the presence of motor complications had higher MDS-UPDRS Part I scores expressing the load of nonmotor features, compared to participants with other disease subtypes or without motor complications. Conclusions. Though the severity of individual nonmotor symptoms was generally rated by PD patients as "mild" or less, we found a significant cumulative effect of nonmotor symptoms on patients' mood, daily activities, and quality of life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".