Evidence‐based treatments for rosacea based on phenotype approach
Bibliographic record
Abstract
Rosacea is a common, chronic skin condition causing flushing, redness, red pimples and pus‐filled spots (pustules) on the face. It affects about 1‐20% of people worldwide. Rosacea can also cause inflammation of the eyes/eyelids (ocular rosacea) and thickening of the skin, especially the nose (rhinophyma). Although the cause of rosacea is unclear, treatments are available for this distressing disease. This review from the Netherlands, U.K. and Canada aimed to find out which treatments are effective for rosacea. The authors included data from 152 studies. For reducing redness, brimonidine and oxymetazoline worked from three up to 12 hours after being applied. For reducing pimples and pustules with topical (applied to the skin) treatments, azelaic acid, ivermectin and metronidazole were effective and safe. Ivermectin was slightly more effective than metronidazole. Minocycline foam also showed a large reduction in pimples and pustules. With oral (taken by mouth) antibiotics, tetracycline, doxycycline 40 mg or minocycline 45 mg reduced the number of pimples and pustules. Doxycycline 40 mg was likely as effective as 100 mg, with fewer side effects like diarrhoea and nausea. Oral minocycline 100 mg was as effective as doxycycline 40 mg. Azithromycin may be as effective as 100 mg doxycycline. Isotretinoin 0.25 mg/kg decreased pimples and pustules by 90%, and increased quality of life and patients’ satisfaction. Isotretinoin 0.3 mg/kg appeared to be slightly more effective than 50‐100 mg doxycycline. However, isotretinoin is known to cause serious birth defects, so pregnancy must be avoided when using it. For treating dilated blood vessels, laser therapy and intense pulsed light therapy were both effective, but these studies had limited data. In ocular rosacea, ciclosporin 0.05% ophthalmic emulsion increased quality of life and improved the amount/quality of tears, and was slightly more effective than oral doxycycline. Omega‐3 fatty acids likely improve dry eyes and tear gland function.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".