Prevalence and predictors of mood disturbances in idiopathic REM sleep behaviour disorder
Bibliographic record
Abstract
Depression and anxiety are commonly associated with synucleinopathies. Mood disturbances have also been reported in patients with idiopathic REM sleep behaviour disorder (iRBD) and are difficult to treat due to exacerbation of sleep symptoms with standard antidepressants. Despite this, detailed prevalence studies of mood symptomatology and contributors to mood disturbances in iRBD are limited. Mood, sleep, autonomic, cognitive and motor symptoms were assessed in 49 well-characterized patients with iRBD using a variety of clinical scales. Spearman correlations, factor analysis and multiple linear regression were used to uncover associations between mood and non-motor and motor symptoms. The prevalence of significant depression was 17.0% and that of anxiety was 14.6% in the iRBD cohort. Age and disease duration were not correlated with these affective symptoms in iRBD patients. We found depression was significantly predicted by the presence and severity of motor, sleep and cognitive symptoms. Anxiety was predicted by the severity of nocturnal and daytime sleep-related symptoms, cognitive symptoms and autonomic symptoms, with a differential effect depending on the questionnaire used. Depression and anxiety are common in iRBD patients and can be significantly explained by specific sets of non-motor and motor symptoms. These associations provide insight into the underlying pathophysiology and emphasize the importance of a holistic approach to mood disturbance in this population, which may circumvent the reliance on pharmacotherapy that can exacerbate dream enactment behaviour.
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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 | 0.002 |
| 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".