Monotherapy for Alopecia Areata: A Systematic Review and Network Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: There are many treatments available for alopecia areata; however, none are approved by the US Food and Drug Administration. Thus, there is clinician benefit in efficacy comparison. METHODS: A network meta-analysis was used to create direct and indirect comparisons of alopecia areata studies in addition to an inconsistency analysis, risk of bias, and quality of evidence assessment. RESULTS: For mild disease, intralesional corticosteroids were ranked the most likely to produce a response at 78.9% according to SUCRA (surface under the cumulative ranking curve) followed by topical corticosteroids (67.9%), prostaglandin analogs (67.1%), diphenylcyclopropenone (DPCP, 63.4%), topical minoxidil (61.2%), and squaric acid dibutylester (SADBE, 35.0%). In contrast, for moderate to severe disease (>50% scalp hair loss), DPCP was the top-ranked treatment (87.9%), followed by laser (77.9%), topical minoxidil (55.5%), topical corticosteroids (50.1%), SADBE (49.7%), and topical tofacitinib (47.6%). There were insufficient eligible trials to include oral tofacitinib in the network. CONCLUSION: Statistically significant evidence is presented for the use of intralesional and topical corticosteroids for treatment of mild disease and DPCP, laser, SADBE, topical minoxidil and topical corticosteroids for moderate to severe disease. Further controlled trials are required to analyze the relative efficacy of oral tofacitinib.
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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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.042 |
| Bibliometrics | 0.008 | 0.007 |
| 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".