Addressing Unmet Needs in Atopic Dermatitis: Evaluating Disease-Modifying Capabilities of Current and Emerging Therapies
Bibliographic record
Abstract
Atopic dermatitis (AD) is a highly burdensome inflammatory skin condition affecting nearly one-quarter of the pediatric population and often continuing into adulthood. Despite recent advancements in systemic therapies providing temporary symptom relief over the past decade, AD frequently remains difficult to control, necessitating increased dosages or alternative treatments due to recurrent disease. This review synthesizes current literature to identify unmet needs of treating AD beyond medication-related limitations and evaluates existing therapies for their efficacy in modifying underlying disease mechanisms. Key findings include variability in AD pathophysiology and phenotypes across different age groups and ethnicities, indicating a need for research into endotype-specific treatments. The literature also comprises evidence suggesting that select current drugs, such as targeted biologics and Janus Kinase (JAK) inhibitors, may offer long-term disease-modifying benefits. Future management strategies should explore novel approaches, including manipulation of the microbiome, immune response, and neural function, as these may lead to additional improvements in AD treatment and long-term symptom relief.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".