Relationship of Nocturnal Sleep Dysfunction and Pain Subtypes in Parkinson's Disease
Bibliographic record
Abstract
Abstract Background Little research has been conducted regarding the relationship between sleep disorders and different pain types in Parkinson's disease (PD). Objective To explore the influence of the various pain subtypes experienced by PD patients on sleep. Methods Three hundred consecutive PD patients were assessed with the PD Sleep Scale‐Version 2 (PDSS‐2), King's PD Pain Scale (KPPS), King's PD Pain Questionnaire (KPPQ), Visual Analog Scales for Pain (VAS‐Pain), and Hospital Anxiety and Depression Scale. Results According to the PDSS‐2, 99.3% of our sample suffered from at least one sleep issue. Those who reported experiencing any modality of pain suffered significantly more from sleep disorders than those who did not (all, P < 0.003). The PDSS‐2 showed moderate‐to‐high correlations with the KPPS (r S = 0.57), KPPQ (0.57), and VAS‐Pain (0.35). When PDSS‐2 items 10 to 12 (pain‐related) were excluded, the correlation values decreased to 0.50, 0.51, and 0.28, respectively, while these items showed moderate‐to‐high correlations with KPPS (0.56), KPPQ (0.54), and VAS‐Pain (0.42). Among the variables analyzed, multiple linear regression models suggested that KPPS and KPPQ were the most relevant predictors of sleep disorders (as per the PDSS‐2), although following exclusion of PDSS‐2 pain items, depression was the relevant predictor. Depression and anxiety were the most relevant predictors in the analysis involving the VAS‐Pain. Regression analysis, considering only the KPPS domains, showed that nocturnal and musculoskeletal pains were the best predictors of overall nocturnal sleep disorder. Conclusions Pain showed a moderate association with nocturnal sleep dysfunction in PD. Some pain subtypes had a greater effect on sleep than others.
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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 | 0.001 | 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) | 0.001 | 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".