Updating the diagnosis, classification and assessment of rosacea: recommendations from the global <scp>ROS</scp> acea <scp>CO</scp> nsensus ( <scp>ROSCO</scp> ) panel
Bibliographic record
Abstract
BACKGROUND: Rosacea is currently diagnosed by consensus-defined primary and secondary features and managed by subtype. However, individual features (phenotypes) can span multiple subtypes, which has implications for clinical practice and research. Adopting a phenotype-led approach may facilitate patient-centred management. OBJECTIVES: To advance clinical practice by obtaining international consensus to establish a phenotype-led rosacea diagnosis and classification scheme with global representation. METHODS: Seventeen dermatologists and three ophthalmologists used a modified Delphi approach to reach consensus on statements pertaining to critical aspects of rosacea diagnosis, classification and severity evaluation. All voting was electronic and blinded. RESULTS: Consensus was achieved for transitioning to a phenotype-based approach to rosacea diagnosis and classification. The following two features were independently considered diagnostic for rosacea: (i) persistent, centrofacial erythema associated with periodic intensification; and (ii) phymatous changes. Flushing, telangiectasia, inflammatory lesions and ocular manifestations were not considered to be individually diagnostic. The panel reached agreement on dimensions for phenotype severity measures and established the importance of assessing the patient burden of rosacea. CONCLUSIONS: The panel recommended an approach for diagnosis and classification of rosacea based on disease phenotype.
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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 | 0.070 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".