Effects of sleep disorders on the non-motor symptoms of Parkinson disease
Bibliographic record
Abstract
Study Objectives: To evaluate the impact of sleep disorders on non-motor symptoms in patients with Parkinson disease (PD). Design: This was a cross-sectional study. Patients with PD were evaluated for obstructive sleep apnea (OSA), restless legs syndrome (RLS), periodic limb movement syndrome (PLMS), and REM sleep behavior disorder (RBD). Cognition was assessed with the Montreal Cognitive Assessment and patients completed self-reported questionnaires assessing non-motor symptoms including depressive symptoms, fatigue, sleep complaints, daytime sleepiness, and quality of life. Setting: Sleep laboratory. Participants: 86 patients with PD (mean age = 67.4 ± 8.8 years; range: 47-89; 29 women). Interventions: N/A. Measurements and Results: Having sleep disorders was a predictor of overall non-motor symptoms in PD (R2= 0.33, p < 0.001) while controlling for age, PD severity, and dopaminergic therapy. These analyses revealed that RBD (p = 0.006) and RLS (p = 0.014) were significant predictors of increased non-motor symptoms, but OSA was not. More specifically, having a sleep disorder significantly predicted sleep complaints (ΔR2= 0.13, p = 0.006), depressive symptoms (ΔR2= 0.01, p = 0.03), fatigue (ΔR2= 0.12, p = 0.007), poor quality of life (ΔR2= 0.13, p = 0.002), and cognitive decline (ΔR2= 0.09, p = 0.036). Additionally, increasing number of sleep disorders (0, 1, or ≥ 2 sleep disorders) was a significant contributor to non-motor symptom impairment (R2= 0.28, p < 0.001). Conclusion: In this study of PD patients, presence of comorbid sleep disorders predicted more non-motor symptoms including increased sleep complaints, more depressive symptoms, lower quality of life, poorer cognition, and more fatigue. RBD and RLS were factors of overall increased non-motor symptoms, but OSA was not.
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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 | 0.001 |
| 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) | 0.002 | 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".