Rosacea treatment update: recommendations from the global <scp>ROS</scp> acea <scp>CO</scp> nsensus ( <scp>ROSCO</scp> ) panel
Bibliographic record
Abstract
BACKGROUND: Rosacea is currently treated according to subtypes. As this does not adequately address the spectrum of clinical presentation (phenotypes), it has implications for patient management. The ROSacea COnsensus panel was established to address this issue. OBJECTIVES: To incorporate current best treatment evidence with clinical experience from an international expert panel and establish consensus to improve outcomes for patients with rosacea. METHODS: Seventeen dermatologists and three ophthalmologists reached consensus on critical aspects of rosacea treatment and management using a modified Delphi approach. The panel voted on statements using the responses 'strongly disagree', 'disagree', 'agree' or 'strongly agree'. Consensus was defined as ≥ 75% 'agree' or 'strongly agree'. All voting was electronic and blinded. RESULTS: The panel agreed on phenotype-based treatments for signs and symptoms presenting in individuals with rosacea. First-line treatments were identified for individual major features of transient and persistent erythema, inflammatory papules/pustules, telangiectasia and phyma, underpinned by general skincare measures. Multiple features in an individual patient can be simultaneously treated with multiple agents. If treatment is inadequate given appropriate duration, another first-line option or the addition of another first-line agent should be considered. Maintenance treatment depends on treatment modality and patient preferences. Ophthalmological referral for all but the mildest ocular features should be considered. Lid hygiene and artificial tears in addition to medications are used to treat ocular rosacea. CONCLUSIONS: Rosacea diagnosis and treatment should be based on clinical presentation. Consensus was achieved to support this approach for rosacea treatment strategies.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".