The study of subjective and objective evaluation of sleep disturbances in Parkinson's disease
Bibliographic record
Abstract
Background Sleep disorder is one of the most common non-motor symptoms in Parkinson's disease (PD) patients. At present, there are subjective and objective tools to evaluate sleep disorders. Nevertheless, previous studies commonly used single subjective questionnaires or objective examinations. Therefore, we used the combinations of subjective and objective tools to analyze clinical characteristics of sleep disturbances in PD and investigated differences and consistence between subjective and objective tools. Methods One hundred and sixteen PD patients were eligible to participate into this study. All participants were evaluated by Pittsburgh Sleep Quality Index (PSQI), Unified Parkinson's Disease Rating Scale (UPDRS) in "on" condition, Hoehn-Yahr (H-Y) stage, Hamilton Depression Rating Scale (HAMD 24 items), Montreal Cognitive Assessment (MoCA), Epworth Sleepiness Scale (ESS), and underwent a video-polysomnography (Video-PSG). Results According to PSQI score of 116 PD patients, the proportion of PD patients with sleep disturbances (PSQI ≥ 7) was 50% (N = 58). Compared to PD patients without sleep disturbances, PD patients with sleep disturbances had lower score of MoCA (23.34 ± 3.50 vs 24.89 ± 3.52; t = 2.377, P = 0.019), higher score of UPDRSⅠ[4.00 (2.00, 5.00) vs 3.00 (2.00, 5.00); U = - 2.306, P = 0.021], UPDRSⅡ[12.00 (9.00, 16.00) vs 10.00 (6.00, 13.00); U = - 1.995, P = 0.046], higher levodopa equivalent daily dose [LED, (508.14 ± 335.85) vs (394.06 ± 236.40) mg/d; t = - 2.115, P = 0.037]. Although PD patients with sleep disturbances had more score of UPDSR Ⅲ and higher H-Y stage, the differences were not significant (P > 0.05). On the other hand, decreased total sleep time (TST), reduced sleep efficiency (SE), increased sleep latency (SL), decreased non-rapid eye movement (NREM) sleep stage Ⅱ time were found for PD patients with sleep disturbances (P < 0.05, for all). Other PSG parameters had no significant differences between PD patients with and without sleep disturbances (P > 0.05, for all). The score of PSQI was positively correlated with the score of ESS (r = 0.200, P = 0.032), HAMD (r = 0.202, P = 0.030), UPDRSⅠ (rs = 0.266, P = 0.004) and Ⅱ (rs = 0.254, P = 0.007), LED (r = 0.213, P = 0.022), SL (rs = 0.211, P = 0.023). Moreover, the score of PSQI was negatively correlated with TST (r = -0.231, P = 0.003), SE (r = -0.192, P = 0.039) and MoCA (r = -0.236, P = 0.011). Conclusion PD patients with sleep disturbances had worse cognition impairment, more mood disorders, decreased activity of daily life. Meanwhile, most of PSG parameters were altered in PD patients with sleep disturbances. Moreover, the severity of sleep disturbances in PD patients was correlated with these factors. Overall sleep quality of PD patients assessed with the objective tool could be predicted by the subjective scale. However, to evaluate sleep architecture and other sleep disorders for PD patients, the objective tools (such as Video-PSG monitoring) are necessary to be used.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".