Bibliographic record
Abstract
Atopic dermatitis is one of the most common skin disorders in the developed world, affecting up to 20% of children and 1% to 3% of adults. This review concisely explains the pathophysiology and epidemiology of atopic dermatitis, as well as potential challenges facing its successful treatment. Furthermore, mainstay topical treatment modalities are evaluated, such as emollients, topical corticosteroids, and topical calcineurin inhibitors. The use of topical corticosteroids and topical calcineurin inhibitors in combination is discussed, as studies have indicated encouraging results. The proactive use of topical corticosteroids and topical calcineurin inhibitors is also investigated, in order to bring attention to a new possibility in long-term management of atopic dermatitis. Last, new and upcoming topical medications are described, including Janus kinase inhibitors, phosphodiesterase-4 inhibitors, and benvitimod. Although topical corticosteroids and topical calcineurin inhibitors can be very effective in the treatment of atopic dermatitis, it is important that practitioners are aware of mechanistically unique and new treatments for patients for whom more traditional topical therapies have failed. Overall, this review article hopes to serve as a comprehensive overview of currently available topical treatments for atopic dermatitis, while shedding light on new treatments coming in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".