Nonmotor Symptoms and Cognitive Decline in <i>de novo</i> Parkinson’s Disease
Bibliographic record
Abstract
BACKGROUND: Cognitive impairments are common in Parkinson's disease (PD). Despite its clinical importance, the development of dementia is still difficult to predict. In this study, we investigated the possible associations between non-motor symptoms and the risk of developing dementia within a 2-year observation period in PD. METHODS: A total of 80 patients with PD participated in this study. Nonmotor symptoms (the Nonmotor Symptoms Questionnaire), PD status (Unified Parkinson's Disease Rating Scale), depression (Geriatric d Depression Scale or Montgomery-Asberg Depression Scale), stereopsis and severity of nonmotor symptoms (Non-motor symptoms scale) were assessed. Global cognitive function (Mini-Mental State Examination) were evaluated at baseline and 2 years later. RESULTS: Presence of depression, vivid dreaming, REM sleep behavior disorders, hyposmia, abnormal stereopsis, non-smoking and postural instability/ gait disturbance phenotype were associated with a significantly more rapid decline of Mini-Mental State Examination. Logistic regression analyses demonstrated that depression (odds ratio=13.895), abnormal stereopsis (odds ratio=10.729), vivid dreaming (odds ratio=4.16), REM sleep behavior disorders (odds ratio=5.353) and hyposmia (odds ratio=4.911) were significant independent predictors of dementia risk within 2 years. Postural instability/ gait disturbance phenotype and age >62 years were also independent predictors of dementia risk (odd ratio=38.333, odds ratio=10.625). CONCLUSIONS: We suggest that depression, vivid dreaming, REM sleep behavior disorders, hyposmia and abnormal stereopsis are closely associated with cognitive decline, and that presence of these nonmotor symptoms predict the subsequent development of Parkinson's disease dementia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".