The relative efficacy of monotherapy with Janus kinase inhibitors, dupilumab and apremilast in adults with alopecia areata: Network meta‐analyses of clinical trials
Bibliographic record
Abstract
BACKGROUND: Janus kinase (JAK) inhibitors, biologics, and phosphodiesterase-4 (PDE-4) inhibitors are recent therapies for alopecia areata (AA)-albeit, knowledge gaps exist for these agents' relative efficacy. OBJECTIVES: We determined the relative efficacy and safety of monotherapy with the aforementioned agents in adults with AA. METHODS: The literature was systematically searched; we used data from randomized trials that investigated the agents' efficacy-as per Severity of Alopecia Tool (SALT) scores. Bayesian network meta-analyses were used to determine relative efficacy and safety. Effect modification was determined using a generalized linear model on aggregate data; evidence quality was evaluated. RESULTS: Based on the surface under the cumulative ranking curve estimates obtained from multiple efficacy endpoints, regimens with the highest likelihood of achieving percent reduction in SALT scores, as well as a minimum 90%, 75% or 50% reduction in SALT scores are (in alphabetical order) baricitinib 4 mg once daily (QD), brepocitinib 60/30 mg QD, deuruxolitinib (CTP-543) 12 mg twice daily (BID), ritlecitinib 200/50 mg QD, ruxolitinib 20 mg BID and tofacitinib 5 mg BID. In contrast, dupilumab subcutaneous injections administered weekly and apremilast 30 mg BID were less likely to be effective. Discontinuation due to any adverse event was the least likely with oral JAK inhibitors, and more likely with dupilumab and apremilast. CONCLUSIONS: Our results support the conduct of high-quality comparative trials to determine whether JAK inhibitors are more effective and safer than PDE4 inhibitors.
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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 | 0.036 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.043 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".