Cannabis Use for Restless Legs Syndrome and Uremic Pruritus in in patients treated with maintenance dialysis: A Survey
Bibliographic record
Abstract
BACKGROUND: Restless legs syndrome (RLS) and uremic pruritus reduce the quality of life in patients with end-stage kidney disease (ESKD) and current treatments are often insufficient. There is an increasing interest in using cannabinoids for symptom management, and preliminary evidence suggests cannabinoids may help alleviate RLS and pruritus. OBJECTIVES: (1) To assess the frequency and severity of RLS and pruritus in our ESKD population with the current treatment options, (2) to estimate patient use of cannabinoids for these symptoms, and (3) to determine interest in participating in future trials to treat RLS and/or pruritus. DESIGN: Survey. PATIENTS: Adult prevalent patients with ESKD treated with dialysis at the Ottawa Hospital. MEASUREMENTS: International RLS Study Group Rating Scale and visual analogue scale for symptom severity of RLS and pruritus, respectively. METHODS: Eligible patients with ESKD treated at the Ottawa Hospital were invited to complete a survey to identify symptoms and severity of RLS and pruritus using validated scales, cannabis use for management, and interest in future trials. Basic demographic statistics to describe the study population and results were used. RESULTS: Sixty-nine percent (192 of 277) of eligible patients completed the surveys, 35 declined participation, and 50 surveys were not returned. Eighty-six (45%) and 129 patients (67%) reported symptoms of RLS and pruritus, respectively. Only 18 previously symptomatic patients were relieved with current treatment. Fifteen patients reported cannabis use for symptoms; 9 noted improvement. Most (>2 of 3) symptomatic patients were interested in participating in a future trial. LIMITATIONS: Single-center study in a tertiary-care hospital in Canada limiting generalizability. Quoted prevalence of symptoms is dependent on survey return. CONCLUSIONS: A large proportion of ESKD patients suffer from RLS and/or pruritus, most of which are not relieved by existing treatments. Few patients reported trying cannabis to decrease their symptoms despite legalization. This study confirms strong patient interest for future trials regarding cannabis for symptom relief. TRIAL REGISTRATION: Not applicable.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".