Rosacea: New Concepts in Classification and Treatment
Bibliographic record
Abstract
Rosacea is a chronic inflammatory dermatosis mainly affecting the cheeks, nose, chin, and forehead. Rosacea is characterized by recurrent episodes of flushing or transient erythema, persistent erythema, phymatous changes, papules, pustules, and telangiectasia. The eyes may also be involved. Due to rosacea affecting the face, it has a profound negative impact on quality of life, self-esteem, and well-being. In addition to general skin care, there are several approved treatment options available for addressing these features, both topical and systemic. For some features, intense pulse light, laser, and surgery are of value. Recent advances in fundamental scientific research have underscored the roles of the innate and adaptive immune systems as well as neurovascular dysregulation underlying the spectrum of clinical features of rosacea. Endogenous and exogenous stimuli may initiate and aggravate several pathways in patients with rosacea. This review covers the new phenotype-based diagnosis and classification system reflecting pathophysiology, and new and emerging treatment options and approaches. We address new topical and systemic formulations, as well as recent evidence on treatment combinations. In addition, ongoing studies investigating novel therapeutic interventions will be summarized.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".