Nonpharmacologic Strategies and Topical Agents for Treating Atopic Dermatitis: An Update
Bibliographic record
Abstract
A s the body of recent literature concerning atopic dermatitis (AD) continues to reshape consensus on many aspects of patient management, clinicians should not neglect to also update our messaging to our patients.It is important for patients and families to understand several key points.First, AD can be safely and effectively controlled in most patients.Second, with the availability of new medications and better strategies for the use of medications and nonpharmacologic regimens that have been the cornerstone of AD management for many years, it is now possible for treatment to improve AD to the point of minimal to no disease and symptoms.Third, flares can be effectively controlled and, in some cases, prevented.Patients and caregivers should know that lack of full control of signs and symptoms of AD need not and should not be simply tolerated, and visits to the clinician should not be put off until a flare occurs or until signs and symptoms progress to an unacceptable threshold of severity.Strong evidence from prospective studies, reviewed and evaluated by a panel of experts, demonstrates that some treatments for AD that have been in use for many years should still be considered valuable components of a comprehensive treatment regimen (Table ).
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 | 0.003 | 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) | 0.005 | 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".