Safety and Efficacy of Rotigotine for Treating Parkinson’s Disease: A Meta-Analysis of Randomised Controlled Trials
Bibliographic record
Abstract
We aimed to comprehensively analyse the safety and efficiency of rotigotine for treating Parkinson's disease (PD). We conducted systematic literature searches of Cochrane library, PubMed and Embase databases up to April 2016, with 'Rotigotine', 'Parkinson Disease ' and 'Parkinson's disease' as key searching terms. Outcomes, including Unified Parkinson's Disease Rating Scale (UPDRS) Part III and Part II scores, 'off' time, adverse events (AEs), serious AEs and discontinuation because of AEs, were compared between rotigotine and placebo groups under a fixed or random effect model. For dichotomous and continuous data, risk ratio (RR) and weighted mean difference with their corresponding 95% confidence intervals (95% CIs) were taken as the effect sizes to calculate merged results. Twelve eligible studies were included. For patients with early or advanced PD, rotigotine could significantly improve UPDRS Part III and Part II scores (p < 0.001) and it had significantly higher incidence of AEs than the placebo (p < 0.001). Regarding discontinuation because of AEs, rotigotine showed a significant advantage over placebo in patients with early PD, whereas the overall result demonstrated no statistically significant difference between the groups. Rotigotine can improve daily living and motor ability of patients with PD, although it has higher incidence of AEs. Rotigotine might be more appropriate for patients with advanced PD than for those with early PD. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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 | 0.036 | 0.060 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.031 | 0.062 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".