An overview of drug discovery efforts for eczema: why is this itch so difficult to scratch?
Bibliographic record
Abstract
Introduction: Atopic dermatitis (AD) is a type of allergic/inflammatory dermatitis characterized by itch and an impairment in quality of life.Areas covered: Herein, the authors review drug discovery efforts for AD, highlighting the clinical efficacy of novel drugs, with a particular focus on the relief of pruritus. Topical agents include emollients, topical antihistamines, corticosteroids, calcineurin inhibitors and herbs. Recently, topical phosphodiesterase E4 (PDE4) inhibitors like crisaborole have become available and are efficacious for mild to moderate AD with few side effects. For more severe AD, monoclonal antibodies like dupilumab are considered as efficacious subcutaneous treatment options. In severe and recalcitrant AD, systemic treatment can ameliorate AD symptoms.Expert opinion: Many topical and systemic medications have demonstrated therapeutic benefits for AD. Indeed, randomized trials have shown that topical PDE4 inhibitors and subcutaneous dupilumab are safe and efficacious. Objective tools to evaluate itch and gauge treatment efficacy is important, but current methodology relies primarily on clinical scores. AD is a systemic atopic disease with a lot of complicated psychosocial issues. Suboptimal efficacy is often due to poor compliance and unrealistic expectation of curative treatment, rendering treatment difficult despite the existence of effective medications.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".