Self-reported depression and anxiety are correlated with functional disability in Parkinson’s disease
Bibliographic record
Abstract
Non-motor symptoms, namely cognitive and affective domain function, may impact the physical functioning and perceived health status of people with Parkinson’s disease (PD). The aim of this cross-sectional observational study was to explore the relationship between the severity of non-motor symptoms (cognitive and affective) and physical function in individuals with PD living in the community. The outcome measures were completed in 19 participants diagnosed with PD, with or without affective symptoms and cognitive impairments. The main constructs included in the bivariate statistical analyses were: self-reported non-motor experiences of daily living (Movement Disorder Society – Unified Parkinson’s Disease Rating Scale [MDS-UPDRS] Part I); self-reported motor experiences of daily living (MDS-UPDRS Part II); clinician-rated impression of motor symptoms (MDS-UPDRS Part III); motor fluctuations (MDS-UPDRS Part IV); self-reported anxiety and depression symptoms (Hospital Anxiety and Depression Rating Scale [HADS] – a total score comprising sub-scores for “anxiety” [HADS-A] and “depression” [HADS-D]); global cognitive function (Montreal Cognitive Assessment [MoCA]); functional gait and balance performance (Dynamic Gait Index [DGI]); and perceived quality of life (European Quality of Life – Visual Analogue Scale [EQ-VAS]). Significant positive correlations (p ≤ 0.05) were observed between the MDS-UPDRS Part II and MDS-UPDRS Part I (p < 0.01), HADS, HADS-A, and HADS-D (p < 0.05). The Hoehn and Yahr (H & Y) scale was the only variable to significantly correlate with the DGI (p < 0.01). Cont...
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".