The long-term treatment of restless legs syndrome/Willis–Ekbom disease: evidence-based guidelines and clinical consensus best practice guidance: a report from the International Restless Legs Syndrome Study Group
Bibliographic record
Abstract
A Task Force was established by the International Restless Legs Syndrome Study Group (IRLSSG) to develop evidence-based and consensus-based recommendations for the long-term pharmacologic treatment of restless legs syndrome/Willis-Ekbom disease (RLS/WED). The Task Force reviewed the results of all studies of RLS/WED treatments with durations of 6 months or longer presented at meetings over the past 2 years, posted on Web sites of pharmaceutical companies, or published in peer-reviewed journals, asking the questions, "What is the efficacy of this treatment in patients with RLS/WED?" and "What is the safety of this treatment in patients with RLS/WED?" The Task Force developed guidelines based on their review of 61 papers meeting inclusion criteria, and using a modified evidence-grading scheme. Pregabalin has been established as effective for up to 1 year in treating RLS/WED (Level A evidence). Pramipexole, ropinirole, and rotigotine have been established as effective for up to 6 months in treating RLS/WED (Level A). The following drugs have been established as probably effective (Level B) in treating RLS/WED for durations ranging from 1 to 5 years: gabapentin enacarbil, pramipexole, and ropinirole (1 year); levodopa (2 years); and rotigotine (5 years). Because of associated safety concerns, pergolide and cabergoline should not be used in the treatment of RLS/WED unless the benefits clearly outweigh the risks. Other pharmacologic therapies have insufficient evidence to support their long-term use in treating RLS/WED. The IRLSSG Task Force also developed consensus-based strategies for the prevention and treatment of complications (such as augmentation, loss of efficacy, excessive daytime sleepiness, and impulse control disorders) that may develop with the long-term pharmacologic treatment of RLS/WED. The use of either a dopamine-receptor agonist or α2δ calcium-channel ligand is recommended as the first-line treatment of RLS/WED for most patients, with the choice of agent dependent on the patient's severity of RLS/WED symptoms, cognitive status, history, and comorbid conditions.
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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 | 0.087 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".