Recent advances in the treatment of uremic pruritus
Bibliographic record
Abstract
PURPOSE OF REVIEW: This article provides a focused update on uremic pruritus, highlighting the latest evidence concerning the epidemiology, pathophysiology, and treatment options for this common and bothersome condition. RECENT FINDINGS: Half of dialysis patients and a quarter of those with nondialysis chronic kidney disease experience bothersome itch that reduces quality of life and is increasingly recognized to be associated with poor outcomes including mortality. The KALM-1 trial, which reported effective symptomatic relief with difelikefalin, has bolstered support for the role of an imbalance of μ and κ-opioid receptor activity in pruritogenesis. The role of a chronic inflammatory state, increased cytokine levels and altered immune signaling in pruritogenic nerve activation continues to be elucidated with basic science, which paves the wave for future novel therapeutics. In the meantime, gabapentin appears to be the most evidence-based widely available uremic pruritus treatment, as long as care is taken with dosing and monitoring of side-effects. SUMMARY: Uremic pruritus remains a top research priority. Patients with uremic pruritus may be able to look forward to a new decade of understanding, knowledge, and novel treatment options for this burdensome condition. As difelikefalin and other potential agents come to market, cost-effectiveness assessments of these interventions will help determine if the widespread use of them is feasible amongst renal programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".