Neuropsychology in REM sleep behaviour disorder and Parkinson’s Disease
Bibliographic record
Abstract
Patients with rapid eye movement (REM) sleep behavior disorder (RBD) experience a loss of muscle atonia during REM sleep. RBD has been associated with a high risk of converting to Parkinson’s disease (PD) within 10 years of diagnosis. This suggests that RBD may be a preclinical indication of neurodegenerative disorders like PD. PD is characterized as a movement disorder, but research has identified nonmotor symptoms (eg. anxiety, depression, and cognitive impairment) that can be used as early disease markers. By examining these nonmotor symptoms in RBD and PD, disease progression may be better understood.\nTo investigate similarities between RBD and PD patients in anxiety, depression, and cognitive impairment severity.\nA case-control study was conducted with 21 healthy controls, 13 RBD, and 21 PD patients enrolled. Participant anxiety, depression, and general cognitive ability were evaluated using Beck Anxiety Index, Beck Depression Index II, and Montreal Cognitive Assessment respectively. Analysis of variance was used to compare questionnaire scores between groups controlling for age and sex as covariates.\nAnxiety levels in PD patients were higher than both RBD and healthy controls. Both patient groups had higher depression scores than controls and depression levels were higher in PD versus RBD patients. Cognitive ability was not different between the three groups.\nResults from this study will further our understanding of the neuropsychological profile of RBD and PD. The discovery of similar nonmotor symptoms between RBD and PD may provide the earliest markers of PD development for improved diagnosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".