Relative Impact of Monotherapies for Vitiligo: A Network Meta‐Analysis Study
Bibliographic record
Abstract
BACKGROUND: Vitiligo, a stigmatizing condition characterized by patchy depigmented skin, has an estimated global prevalence of 0.36%. This condition is a risk factor for anxiety, depression, and even suicidal ideation. Hitherto, no network meta-analysis has investigated the relative effect of vitiligo on relevant monotherapies. AIM: The current study determined the relative effect of monotherapies for vitiligo through network meta-analyses (NMAs). METHODS: The peer-reviewed literature was systematically searched through PubMed and Scopus; studies that were eligible for quantitative analyses were those that were published in English and had an arm that investigated the effect of a monotherapy on vitiligo at 6 months. Studies of the randomized and observational designs were included. RESULTS: The retrieved data were sufficient to analyze networks for phototherapy, Janus kinase inhibitors (JAKIs), calcineurin inhibitors, cyclosporine, corticosteroids, azathioprine, and minocycline. Modalities' effects, in each of the networks, were ranked with the surface under the cumulative ranking curve (SUCRA) metric; league tables were produced to depict agents' pairwise relative effects. Our secondary outcome was discontinuation due to any adverse event (AE) at 6 months. CONCLUSIONS: No significant differences are observed among JAK inhibitors; however, upadacitinib, cyclosporine, ritlecitinib, and dexamethasone are significantly more effective than minocycline. Psoralen (oral) + ultraviolet A (PUVA) and narrow band ultraviolet B (NB-UVB) regimens show similar efficacy for repigmentation. Ruxolitinib 1.5% cream (once or twice daily), ruxolitinib 0.5% cream once daily, and ruxolitinib 0.15% cream once daily for 6 months do not differ significantly in efficacy. Mometasone furoate and tacrolimus 0.1% ointment are more effective than tacrolimus 0.03%.
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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 | 0.021 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.046 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".