Recommendations for rosacea diagnosis, classification and management: update from the global <scp>ROS</scp> acea <scp>CO</scp> nsensus 2019 panel
Bibliographic record
Abstract
BACKGROUND: A transition from a subtyping to a phenotyping approach in rosacea is underway, allowing individual patient management according to presenting features instead of categorization by predefined subtypes. The ROSacea COnsensus (ROSCO) 2017 recommendations further support this transition and align with guidance from other working groups. OBJECTIVES: To update and extend previous global ROSCO recommendations in line with the latest research and continue supporting uptake of the phenotype approach in rosacea through clinical tool development. METHODS: Nineteen dermatologists and two ophthalmologists used a modified Delphi approach to reach consensus on statements pertaining to critical aspects of rosacea diagnosis, classification and management. Voting was electronic and blinded. RESULTS: Delphi statements on which the panel achieved consensus of ≥ 75% voting 'Agree' or 'Strongly agree' are presented. The panel recommends discussing disease burden with patients during consultations, using four questions to assist conversations. The primary treatment objective should be achievement of complete clearance, owing to previously established clinical benefits for patients. Cutaneous and ocular features are defined. Treatments have been reassessed in line with recent evidence and the prior treatment algorithm updated. Combination therapy is recommended to benefit patients with multiple features. Ongoing monitoring and dialogue should take place between physician and patients, covering defined factors to maximize outcomes. A prototype clinical tool (Rosacea Tracker) and patient case studies have been developed from consensus statements. CONCLUSIONS: The current survey updates previous recommendations as a basis for local guideline development and provides clinical tools to facilitate a phenotype approach in practice and improve rosacea patient management. What's already known about this topic? A transition to a phenotype approach in rosacea is underway and is being recommended by multiple working groups. New research has become available since the previous ROSCO consensus, necessitating an update and extension of recommendations. What does this study add? We offer updated global recommendations for clinical practice that account for recent research, to continue supporting the transition to a phenotype approach in rosacea. We present prototype clinical tools to facilitate use of the phenotype approach in practice and improve management of patients with 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 | 0.032 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".