Efficacy of Treatments in Reducing Facial Erythema in Rosacea: A Systematic Review
Bibliographic record
Abstract
Rosacea is a chronic inflammatory skin condition that affects over 5% of individuals worldwide. Its clinical presentation is characterized by an array of features, including erythema, papules and pustules, phymatous changes, telangiectasia, and ocular manifestations. Specifically, the multifaceted manifestation of erythema varies widely in intensity and distribution. Factors contributing to pathogenesis include neurovascular dysregulation, increased levels of pro-inflammatory mediators, and aberrant vasodilation. Erythema management plays an important role in reducing the psychosocial burden associated with rosacea and improving overall quality of life. Cochrane CENTRAL, Medline, and Embase databases were searched from inception to September 2023 and included 33 clinical trials reporting on a total of 7411 rosacea patients (74.1% female) and 21 different topical or systemic treatments. The mean age was 48.8 years (range, 18-83 years), and the mean time to outcome assessment was 8.1 weeks (standard deviation, 4.1 weeks). Treatment efficacy was assessed by outcome measures including percent improvement from baseline on 4- and 5-point scales, clinician erythema assessment (CEA) success (improvement ≥1 point), and CEA and patient self-assessment success (improvement ≥1 point). Pooled effect sizes for each treatment were calculated as a weighted average based on the number of patients in each study. The most effective topical treatments for reducing erythema include sodium sulphacetamide and sulphur, praziquantel, metronidazole, and B244 spray ( Nitrosomonas eutropha ). The most effective systemic treatment was paroxetine. Our findings highlight the varying efficacy of treatments in addressing the erythema in rosacea, recognizing the nuances of clinical presentations.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".