Treatment with Rasagiline Improves the Quality of Sleep in Patients with Parkinson's Disease: Results of the Rasagiline Effect on Sleep Trial (REST) (S52.006)
Bibliographic record
Abstract
Objective: To evaluate the impact of rasagiline treatment on sleep disturbances in Parkinson Disease. Background Sleep dysfunction in PD is common affecting some 60–98% of patients. Sleep disorders include excessive daytime sleepiness, sleep attacks, advanced sleep phase syndrome, nocturnal awakenings, and REM sleep behaviour disorder – each having a significant impact on patient quality of life. Design/Methods: This was an open-label, multi-center, single-arm study in patients with PD who were considered suitable for treatment with rasagiline as monotherapy or adjunct therapy (0.5 or 1.0 mg once daily per Canadian label). Subjects were assessed at baseline and after 2 months of treatment using the Parkinson9s Disease Sleep Scale (PDSS) to assess overall sleep quality and Epworth Sleepiness Scale (ESS) to assess daytime sleepiness. Results: 110 PD patients were treated with rasagiline (mean age 67 years; disease duration 4.3 years) and 97 completed the two visits; most had a Hoehn and Yahr Stage of 2 and received rasagiline as adjunct therapy. Treatment with rasagiline improved mean ± SD PDSS scores from 96.2 ± 21.6 at baseline to 105.5 ± 21.93 at 2 months (treatment effect 9.1 ± 18.7 points, p=0.003 [n=97]), denoting an improvement in sleep experience. Analysis by item revealed significant differences from baseline in overall quality of sleep, nocturnal restlessness, nocturnal motor symptoms and sleep refreshment (p Conclusions: In this open-label study, two months treatment with rasagiline improved sleep experience in patients with PD. Supported by: Teva Canada Innovation. Disclosure: Dr. Panisset has received personal compensation for activities with Teva Neuroscience, Novartis, Allergan, and Merz.Dr. Panisset has received research support from Teva Neuroscience, Novartis, and Allergan. Dr. Chouinard has received personal compensation for activities with Teva Neuroscience, Novartis, Shires, and Prestwick. Dr. Chouinard has received personal compensation in an editorial capacity from Novartis. Dr. Chouinard has received research support from Novartis, Teva Neuroscience, Elan Corporation, Schering-Plough Corporation, Merck & Co., Inc., Kyowa, and Amarin.
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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 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".