Lack of Evidence for the Efficacy of Antifungal Medications to Treat Atopic Dermatitis: A Systematic Review
Bibliographic record
Abstract
ABSTRACT Background Atopic dermatitis (AD) is characterised by immune dysregulation and skin‐barrier dysfunction, which can predispose to microbial dysbiosis and skin infections. Fungi, particularly Malassezia, are common skin colonisers in AD patients and may exacerbate the condition. Previous studies on the efficacy of antifungal treatments for AD have shown inconsistent results. Objectives This systematic review aims to evaluate the effectiveness of topical and oral antifungal therapies in treating AD. Methods A systematic review was conducted following the PRISMA guidelines (PROSPERO ID: CRD42022338791). Databases searched included PubMed, MEDLINE, Embase, Scopus, LILACS, Cochrane, CINAHL, GREAT, and ClinicalTrials.gov . Inclusion criteria encompassed interventional studies using antifungal agents for AD, with outcomes such as SCORing Atopic Dermatitis (SCORAD). Results Of 35,591 studies obtained from the initial search, seven met the inclusion criteria. Topical antifungal treatments, such as sertaconazole, miconazole, and ciclopirox olamine, showed minimal efficacy in reducing AD severity when compared to placebo or hydrocortisone treatments. Oral antifungal treatments, including itraconazole and ketoconazole, demonstrated mixed results, with some studies showing modest improvements in SCORAD scores but overall lacking significant clinical benefit. Conclusions The systematic review found weak evidence supporting the efficacy of both topical and oral antifungal therapies for treating AD. Topical antifungals showed limited improvement and fewer adverse effects, while oral antifungals posed a higher risk of systemic adverse events without consistent efficacy. Based on these findings, antifungal treatments are not recommended for AD severity management alone other than for treatment of comorbid fungal and yeast infections. Future research should focus on larger sample sizes, standardised severity assessments, and comprehensive adverse event reporting to better evaluate antifungal treatments in AD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.243 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".