THE EFFECTIVENESS OF PHARMACOLOGICAL AND NON-PHARMACOLOGICAL INTERVENTIONS IN IMPROVING RESTLESS LEGS SYNDROME SEVERITY AMONG PATIENTS ON DIALYSIS: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Background and objectives Restless legs syndrome (RLS) is common amongst patients receiving dialysis. It is unclear how to optimally treat RLS in dialysis patients. We conducted a systematic review to identify therapies to reduce the severity of RLS. Methods We systematically searched MEDLINE and EMBASE and hand searched narrative reviews for randomized controlled trials that assessed the use of pharmacological or non-pharmacological interventions to reduce the severity of RLS among patients receiving dialysis. The primary outcome was RLS symptoms. Results Of 621 abstracts identified, 12 studies that included 13 interventions met the inclusion and exclusion criteria. All trials reported interventions that significantly improved RLS severity. The interventions included dopamine agonists (n=6), gabapentin (n=2), intradialytic exercise (n=2) intravenous iron (n=1), vitamins C and E (n=1), and clonidine (n=1). In studies that compared two interventions, ropinirole gabapentin, and intradialytic exercise with resistance were all more effective than L-dopa in comparisons. However, all studies were at high risk of bias with the most common causes due to lack of allocation concealment, high losses to follow-up, and missing data. Further, follow-up for these studies was short-term ranging from 3 days to 24 weeks. Conclusions Ropinirole, gabapentin, and intradialytic exercise appear promising to reduce RLS in dialysis patients. However, methodologically rigorous, appropriately powered, longer term RCTs are required to determine the optimal treatment strategy for RLS among dialysis patients.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".