The Role of IL-17 in Papulopustular Rosacea and Future Directions
Bibliographic record
Abstract
Rosacea is a chronic, progressive, inflammatory condition phenotypically subtyped into diagnostic features, major features, and minor/secondary features. There is currently no cure for rosacea, and it carries a significant negative psychosocial burden for afflicted patients. While there are a number of treatment modalities at the disposal of the clinician, clinical experience has suggested a need for updated treatments. The pathogenesis of rosacea is multifactorial; however, this paper will focus on the pivotal role of interleukin 17 (IL-17) in the development and progression of the disease. Furthermore, this paper will explore the mechanism of action of standard rosacea treatments and their effect on different stages of the IL-17 pathway. The standard treatments for rosacea are usually effective in controlling the symptoms of the disease in its mild-to-moderate form; however, their efficacy is diminished in the setting of severe and treatment-resistant rosacea. We hypothesize that IL-17 inhibitors, currently used successfully in psoriasis and psoriatic arthritis, could perhaps be used to treat severe and treatment-resistant papulopustular rosacea in the future; however, clinical trials and case reports will be needed to dictate expanded indications of IL-17 inhibitors. Furthermore, the high cost of IL-17 inhibitors presently prevents their use in disease states other than psoriasis or psoriatic arthritis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".