Roflumilast in seborrheic dermatitis: Review
Bibliographic record
Abstract
Seborrheic dermatitis (SD) is a chronic skin condition that can significantly impact patients’ quality of life. While topical antifungals and corticosteroids are often used, they frequently provide only a partial response or may lead to side effects with long-term use. The objective of our study was to evaluate the efficacy and safety of topical phosphodiesterase-4 inhibitor (0.3% roflumilast foam [ROF]) in the treatment of SD. We conducted a comprehensive literature search in PubMed, Embase, Scopus, and Cochrane databases until May 2024. Our inclusion criteria were randomized, non-randomized, and open-label trials that compared ROF with a control; patients with SD treated with ROF; and reported relevant outcomes. We excluded studies with overlapping populations. The risk of bias was assessed using Cochrane’s guidelines. R programming was used to evaluate the risk ratio using a random-effects model and assess heterogeneity. Our analysis included three studies comprising a total of 1083 patients. The pooled results at week 4 showed that 53.64% (95% CI, 30.25%–77.03%; p = 0.88; I 2 = 98%) of patients in the ROF group achieved Investigator Global Assessment success, and 53.91% (95% CI, 47.40%–60.43%; p = 0.55; I 2 = 72%) achieved Worst Itch Numeric Rating Scale success. Furthermore, a subgroup analysis of randomized controlled trials (RCTs) was performed, which reduced the heterogeneity and demonstrated the significant efficacy of ROF in the treatment of SD. The key limitations of our study were the short follow-up period and the high degree of heterogeneity in our pooled analysis. We concluded that ROF shows promise for the treatment of SD, but further RCTs with extended follow-up periods are needed to fully evaluate its efficacy. This review was prospectively registered on PROSPERO (ID-CRD42024546079) and was conducted without any funding support.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".