Effect of Psychiatric and Other Nonmotor Symptoms on Disability in Parkinson's Disease
Bibliographic record
Abstract
OBJECTIVES: To examine the effect of depression and other nonmotor symptoms on functional ability in Parkinson's disease (PD). DESIGN: A cross-sectional study of a convenience sample of PD patients receiving specialty care. SETTING: The Parkinson's Disease Research, Education and Clinical Center at the Philadelphia Veterans Affairs Medical Center. PARTICIPANTS: One hundred fourteen community-dwelling patients with idiopathic PD. MEASUREMENTS: The Unified Parkinson's Disease Rating Scale (UPDRS); Hoehn and Yahr Stage; Mini-Mental State Examination; Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, depression module; probes for psychotic symptoms; Hamilton Depression Rating Scale; Geriatric Depression Scale-Short Form; Apathy Scale; and Epworth Sleepiness Scale. Disability was rated using the UPDRS activity of daily living (ADL) score and the Schwab and England ADL score. Multivariate analysis determined effect of depression and other nonmotor symptoms on disability. RESULTS: The presence of psychosis, depressive disorder, increasing depression severity, age, duration of PD, cognitive impairment, apathy, sleepiness, motor impairment, and percentage of time with dyskinesias were related to greater disability in bivariate analyses. Entering these factors into two multiple regression analyses, only the increasing severity of depression and worsening cognition were associated with greater disability using the UPDRS ADL score, accounting for 37% of the variance in disability (P<.001). These two factors plus increasing severity of PD accounted for 54% of the variance in disability using the Schwab and England ADL score (P<.001). CONCLUSION: Results support and extend previous findings that psychiatric and other nonmotor symptoms contribute significantly to disability in PD. Screening for nonmotor symptoms in PD is necessary to more fully explain functional limitations. Further study is required to determine whether identifying and treating these symptoms will improve function and quality of life.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".