Treatment of Uremic Pruritus: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Uremic pruritus is a common and burdensome symptom afflicting patients with advanced chronic kidney disease (CKD) and has been declared a priority for CKD research by patients. The optimal treatments for uremic pruritus are not well defined. STUDY DESIGN: Systematic review. SETTING & POPULATION: Adult patients with advanced CKD (stage ≥ 3) or receiving any form of dialysis. SELECTION CRITERIA FOR STUDIES: PubMed, CINAHL, Embase, International Pharmaceutical Abstracts, Scopus, Cochrane Library, and ClinicalTrials.gov from their inception to March 6, 2017, were systematically searched for randomized controlled trials (RCTs) of uremic pruritus treatments in patients with advanced CKD (stage ≥ 3) or receiving any form of dialysis. 2 reviewers extracted data independently. Risk of bias was assessed using the Cochrane Collaboration risk-of-bias tool. INTERVENTION: Any intervention for the treatment of uremic pruritus was included. OUTCOMES: A quantitative change in pruritus intensity on a visual analogue, verbal rating, or numerical rating scale. RESULTS: 44 RCTs examining 39 different treatments were included in the review. These treatments included gabapentin, pregabalin, mast cell stabilizers, phototherapy, hemodialysis modifications, and multiple other systemic and topical treatments. The largest body of evidence was found for the effectiveness of gabapentin. Due to the limited number of trials for the other treatments included, we are unable to comment on their efficacy. Risk of bias in most studies was high. LIMITATIONS: Heterogeneity in design, treatments, and outcome measures rendered comparisons difficult and precluded meta-analysis. CONCLUSIONS: Despite the acknowledged importance of uremic pruritus to patients, with the exception of gabapentin, the current evidence for treatments is weak. Large, simple, rigorous, multiarm RCTs of promising therapies are urgently needed.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".