Targeting the Opioid Pathway for Uremic Pruritus
Bibliographic record
Abstract
BACKGROUND: Patients undergoing hemodialysis or peritoneal dialysis often experience pruritus which is associated with morbidity and mortality. One proposed treatment approach is to target the opioid pathway using either µ-opioid antagonists or κ-opioid agonists. OBJECTIVE: To review the efficacy of targeting the opioid pathway for pruritus among dialysis patients (uremic pruritus). DESIGN: Systematic review and meta-analysis. SETTING/METHODS: The systematic review included randomized controlled and randomized crossover trials identified in the MEDLINE, EMBASE, and Cochrane databases (1990 to June 2014) evaluating the efficacy of µ-opioid antagonists or κ-opioid agonists in the treatment of uremic pruritus. PATIENTS: Adult (≥18 years) chronic dialysis patients. MEASUREMENTS: The primary outcome being evaluated was reduction in itch severity measured on a patient-reported visual analog scale (VAS). RESULTS: Five studies out of 3587 screened articles met the inclusion criteria. Three studies evaluated the efficacy of naltrexone, a µ-opioid antagonist, and 2 studies evaluated the efficacy of nalfurafine, a κ-opioid agonist. Duration of included studies was short, ranging from 2 to 9 weeks. LIMITATIONS: < .001) greater reduction of itch severity (measured on a 100-mm VAS) than placebo in the treatment of uremic pruritus. CONCLUSIONS: Nalfurafine holds some promise with respect to the treatment of uremic pruritus among dialysis patients. However, more long-term randomized controlled trials evaluating the efficacy of therapies targeting the opioid pathway for uremic pruritus are required.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".