Ropinirole treatment for restless legs syndrome
Bibliographic record
Abstract
In this paper we discuss therapy with ropinirole (known as adartrel in the United Kingdom) in patients with restless legs syndrome. Restless legs syndrome is characterized by an urge to move the legs, uncomfortable sensations in the legs and worsening of these symptoms during rest with at least temporary relief brought on by activity. Current recommendations suggest dopaminergic therapy (levodopa or dopamine receptor agonists like ropinirole) as the first-line treatment for restless legs syndrome. Based on the results of randomized, placebo-controlled, double-blind trials, we conclude that ropinirole is effective in reducing symptoms of restless legs syndrome in the general population. Ropinirole has no serious or common side effects that would limit its use significantly. Rebound and augmentation problems are relatively rarely seen with ropinirole, although properly designed comparative trials are still needed to address this question. It must be noted, however, that most published studies with ropinirole compare this drug with placebo. Very few studies have compared ropinirole with other drugs (L-dopa, gabapentin, opioids, benzodiazepines, other dopaminergic agents and selegiline hydrochloride). No cost-effectiveness trial has been published yet. Treatment of restless legs syndrome with ropinirole shows it to be effective, well-tolerated and safe and it can be used in restless legs syndrome in general.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".