Exploring Newer Topical Therapies for Inflammatory Skin Diseases: A Guide for Rheumatologists
Bibliographic record
Abstract
Understanding the pathogenesis of many inflammatory skin diseases and their associated signalling pathways has revealed multiple promising therapeutic targets. Given the chronic nature of many of these conditions, products with long-term safety and efficacy are desired. While topical corticosteroids have been the mainstay of topical therapies for years, they are burdened by concerns over long-term safety (i.e., atrophy, striae, telangiectasias), risk of absorption with systemic glucocorticoid side effects, and patient apprehension regarding steroid use. Similarly, topical calcipotriol and retinoids may be ineffective and can cause irritation. Although topical calcineurin inhibitors (i.e., pimecrolimus, tacrolimus) have been approved for atopic dermatitis, their off-label use for many inflammatory conditions may be limited by tolerability issues such as stinging and burning, and lack of effectiveness. The emergence of newer targeted small molecules for topical application, including topical phosphodiesterase-4 inhibitors (PDE4i), topical Janus kinase inhibitors (JAKi), and a therapeutic aryl hydrocarbon modulating agent (TAMA), offer promising new options and will be reviewed here and summarized in Table 1.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".