A review of the management of uremic pruritus: current perspectives and future directions
Bibliographic record
Abstract
Uremic pruritus (UP) is a common and distressing symptom experienced by up to half of all patients with end-stage renal disease (ESRD) receiving dialysis. It is associated with multiple health-related quality of life impairments and has been independently associated with mortality. Despite the prevalence and associated impact on quality of life, UP remains a difficult symptom to treat because of the relative lack of existing high quality evidence on which to base recommendations and the sheer volume of poorly studied therapeutic options. This review outlines the existing data of available treatment options including topical therapy, systemic therapy, and phototherapy as well as explore emerging data on therapies that are targeting novel pruritus pathways including the cannabinoid and opioid pathways. Overall, neuromodulators, in particular gabapentin, appear to have the most robust data in the treatment of UP. In individuals who cannot tolerate oral systemic therapy or in those with refractory generalized UP, ultraviolet phototherapy, specifically broad-band UVB, has shown significant promise. However, access is often a limiting factor. Lastly, the emergence of new therapies targeting a peripheral acting κ-opioid agonist, difelikefalin, has demonstrated effect in both early phase 2 and 3 clinical trials.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".