Nonergot dopamine-receptor agonists for treating Parkinson's disease – a network meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To compare the efficacy of the three nonergot dopamine-receptor agonists (DAs) pramipexole, ropinirole, and rotigotine for the treatment of early and advanced Parkinson's disease (PD). MATERIALS AND METHODS: Bayesian network meta-analyses were performed separately for early and advanced PD, and at time points 11-16 and 24-28 weeks. Outcomes for early PD included improvement on the Unified Parkinson's Disease Rating Scale (UPDRS) activities in daily life (UPDRS-II), motor function (UPDRS-III), and their subtotal (UPDRS-II + III). Outcomes for advanced PD also included daily "off time" (hours), but not UPDRS-II + III. RESULTS: Totals of 23 and 24 trials informed early and advanced PD analyses. For early PD UPDRS-II at 11-16 weeks, pramipexole and rotigotine were statistically significantly superior to placebo, but ropinirole was not. For UPDRS-III and UPDRS-II + III, all DAs were statistically significantly better than placebo and exhibited similar improvements. At 24-28 weeks, results were also statistically significant for all DAs versus placebo, and the magnitudes of improvements were similar for pramipexole, ropinirole and rotigotine. Advanced PD improvements on UPDRS-II, UPRDS-III, and off time were statistically significant for pramipexole, ropinirole, and rotigotine versus placebo. At 11-16 weeks, rotigotine yielded slightly smaller effects than ropinirole and pramipexole, but credible intervals on differences were wide. For off time, results were near identical. At 24-28 weeks, results were similar for all three outcomes. Ropinirole yielded a slightly higher improvement on UPDRS-III, but a slightly smaller improvement in off time. CONCLUSION: Our analyses suggest that pramipexole, ropinirole, and rotigotine exhibit similar efficacy in the treatment of early and advanced 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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.028 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".