Efficacy of Janus kinase inhibitor combined with phototherapy in non-segmental vitiligo: systematic review and meta-analysis
Bibliographic record
Abstract
Background Vitiligo is a chronic autoimmune disease causing skin depigmentation and psychosocial issues. Non-segmental vitiligo often shows limited response to standard therapies. The IFN-γ–JAK–STAT pathway plays a key role, and combining JAK inhibitors with NB-UVB may improve repigmentation.Aim To evaluate the efficacy of JAK inhibitors (baricitinib or tofacitinib) plus NB-UVB versus NB-UVB without JAK inhibitors in adult NSV.Methods PubMed, Cochrane, and ClinicalTrials.gov were searched till August 2024 for randomized controlled trials. Four studies, comprising 217 adults (121 in the combination group and 96 in the control group), were analyzed using RevMan 5.4. Bias was assessed via Newcastle-Ottawa, Cochrane ROB-2, and ROBINS-I tools.Results Combination therapy significantly reduced total VASI compared to controls (MD = −4.96, 95% CI [–9.29, −0.63], p = 0.02), with a greater effect on sensitivity analysis (MD = −6.84, p = 0.0007). Significant reductions were seen in face/neck (MD = −0.17, p = 0.002), trunk (MD = −3.62, p = 0.0001); acral (MD = −0.85, p = 0.0002) and extremity (MD = −4.61, p < 0.00001) regions after sensitivity analysis. Patients were more likely to achieve ≥50% (RR = 6.87, p < 0.00001) and ≥75% (RR = 15.13, p = 0.006) repigmentation.Conclusions JAK inhibitor plus NB-UVB markedly improves the repigmentation in adult NSV compared to NB-UVB alone.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".