The Relative Efficacy and Safety of Monotherapies for Alopecia Areata: A Network Meta‐Analysis Study
Bibliographic record
Abstract
ABSTRACT Background Scant evidence exists for the relative efficacy of therapies for alopecia areata (AA)—including those approved by the Food and Drug Administration, namely, baricitinib, deuruxolitinib, and ritlecitinib. Aims We determined the relative efficacy and safety of monotherapy with janus kinase inhibitors (JAKIs), apremilast, and dupilumab. Methods Following a systematic review, we conducted Bayesian network meta‐analysis (NMAs) that produced Surface Under the Cumulative RAnking (SUCRA) values and point estimates for pairwise relative effects; we also performed sensitivity analyses. Results In total, regimens with eight various JAKIs were compared, namely, ruxolitinib, ATI‐501, baricitinib, brepocitinib, deuruxolitinib, ivarmacitinib, ritlecitinib, and tofacitinib. Our analyses ranked “deuruxolitinib 12 mg twice daily for 24 weeks,” the most efficacious insofar as “proportion of participants achieving SALT ≤ 20 at 24 weeks” (SALT20) (SUCRA = 92.6%), and “proportion of participants achieving SALT ≤ 10 at 24 weeks” (SALT10) (SUCRA = 97.7%). As per SALT20, the highest‐ranked regimen was more efficacious than “baricitinib 2 mg once daily for 24 weeks” (odds ratio [OR] = 5.37, 95% credible interval [CI] = 1.59, 13.70, p < 0.05). Furthermore, the efficacy of the FDA‐approved JAKIs exhibited a dose‐dependent relationship; for instance, baricitinib 4 mg once daily for 24 weeks was more efficacious than “baricitinib 2 mg once daily for 24 weeks” in terms of SALT20 (OR = 2.25, 95% CI = 1.56, 3.21, p < 0.05). Results from our sensitivity analyses support that our base analyses were robust. Conclusions We produced high‐quality evidence on the comparative effectiveness of monotherapies for AA with various regimens of 8 JAKIs, including the FDA‐approved ones. Our findings can improve clinicians' decision‐making and update guidelines for medical practice.
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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 | 0.029 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.040 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".