Efficacy of Treatments in Reducing Inflammatory Lesion Count in Rosacea: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Rosacea is a chronic inflammatory skin condition affecting approximately 5.5% of the global population. Patients present heterogeneously with a mix of features in the central facial region, of which papules and pustules are considered to be a major feature. The identification of effective treatments for reducing inflammatory lesions in rosacea can alleviate the psychosocial burden that many rosacea patients experience, including reduced self-esteem, anxiety, and social withdrawal. The objective of this systematic review is to determine the effectiveness of topical and systemic therapies in reducing lesion count in rosacea patients. METHODS/RESULTS: Medline, Embase, and Cochrane CENTRAL databases were searched, resulting in the inclusion of 43 clinical trials reporting on a total of 18,347 rosacea patients. The most well-studied treatments include ivermectin, metronidazole, azelaic acid, minocycline, and doxycycline. Oral isotretinoin was the most effective treatment in reducing inflammatory lesions and may be recommended for severe recalcitrant cases of rosacea. CONCLUSIONS: mites. Additional research is required to determine effective combination therapies in rosacea.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.005 | 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) | 0.006 | 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".