Association of restless legs syndrome, pain, and mood disorders in parkinson's disease
Bibliographic record
Abstract
PURPOSE/AIMS: The objectives of the study were to analyze the association between Parkinson's disease and restless legs syndrome, and to explore the relationship between mood disorder comorbidity (anxiety and depression), pain, and restless legs syndrome. METHODS: This study included 123 Parkinson's disease patients and 123 non-Parkinson's disease patients matched for age and gender, and evaluated for anxiety severity, depression severity, pain severity, pain interference, pain disability, and restless legs syndrome prevalence. This was performed using semi-structured interviews and a neurological examination through the restless legs syndrome diagnostic criteria and the following inventories; Hospital Anxiety and Depression Scale, Brief Pain Inventory, and Pain Disability Index. RESULTS: Parkinson's disease patients had significantly greater anxiety severity, depression severity, pain severity, pain interference, pain disability, and restless legs syndrome prevalence in comparison to controls. In addition, Parkinson's disease patients' comorbid for anxiety and depression had significantly greater pain severity, pain interference, and pain disability, but not RLS prevalence, in comparison to Parkinson's disease only, Parkinson's disease anxiety, and Parkinson's disease depression patients. CONCLUSIONS: Pain interference, pain severity, and pain disability is greater among Parkinson's disease patients with anxiety and depression, in comparison to Parkinson's disease patients without anxiety and depression. On the contrary, the prevalence of restless legs syndrome was not found to be relevant.
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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 | 0.000 | 0.001 |
| 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".