The Effect of Rotigotine on Cognitive Function and Sleep Problems in Parkinson's Disease: an Open-Label Pilot Study
Bibliographic record
Abstract
Abstract Background: We hypothesized that rotigotine may have a positive effect on cognitive function in patients with Parkinson’s disease (PD) by improving daytime motor function and nighttime sleep status due to its 24-hour sustained properties.Methods: We evaluated the effect of rotigotine on motor symptoms, cognitive function, daytime sleepiness, sleep disturbances, and motor symptoms in 10 PD patients with sleep disturbances, defined as a PD Sleep Scale (PDSS)-2 score of ≥ 15, in a single-center, 3-month open-label study. Participants received 24 mg/24 h (patch content: 4.5-9 mg) rotigotine for a 3-month period. At baseline and 3 months, patients were evaluated on the Movement Disorder Society Revision of the Unified PD Rating Scale (MDS-UPDRS) parts III and IV and cognitive assessments, such as the Mini-Mental State Examination (MMSE), frontal assessment battery (FAB) and Montreal Cognitive Assessment (MoCA). The Epworth Sleepiness Scale (ESS) and PDSS-2 were administered at baseline and at 1 month, 2 months and 3 months.Results: At 3 months, MDS-UPDRS part III (-10.7, p<0.001) and MDS-UPDRS part IV (-1.0, p=0.023) scores significantly decreased, MoCA scores (1.7, p=0.0095) significantly increased, and off time significantly decreased (-43.0 min; p=0.029) from baseline. PDSS-2 scores significantly decreased from baseline at 2 months (-14.5, p<0.05) and 3 months (-20.0, p<0.001). ESS, MMSE or FAB scores did not significantly change after rotigotine treatment.Conclusion: Our preliminary findings suggest that low-dose rotigotine could improve motor symptoms, sleep disturbance, and cognitive function without worsening daytime sleepiness in patients with PD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".