New and Emerging Pharmacotherapies for Pruritus: A Systematic Review and Network Meta-Analysis
Bibliographic record
Abstract
Chronic pruritus, a common symptom of dermatologic diseases, impairs quality of life. No review exists that evaluates the efficacy of emerging therapies based on a single standardized scale. We assessed efficacy and adverse effects of novel treatments available for pruritus in patients diagnosed with common pruritic diseases using the proportions of patients experiencing a ≥4-point reduction as a standardized efficacy measure between studies. We searched PubMed, Web of Science, Embase, and Cochrane Central Register of Controlled Trials for phase II or III trials reporting change in numerical rating scale following interventions for pruritus from 2015 to 2023. The strongest improvements in pruritus were seen from ustekinumab (RR 4.30 [2.88; 6.41]) and ixekizumab (RR 4.42 [3.32; 5.88]) for psoriasis and upadacitinib (RR 5.54 [95% CI: 4.53-6.78]), abrocitinib (RR 3.76 [95% CI: 2.97-4.76]), and baricitinib (RR 3.63 [95% CI: 2.36-5.58]) for atopic dermatitis. Nemolizumab (RR 3.06 [95% CI: 1.63-5.74]) and dupilumab (RR 2.11 [95% CI: 1.30-3.41]) were most effective for prurigo nodularis, though fewer studies were available for comparison. Adverse effects were mild and similar between agents; discontinuation rates were low. This review evaluates efficacy of emerging pruritus treatments based on a single standardized measurement, highlighting the role of novel agents in the treatment of chronic pruritus.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.042 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".