Approach to the Assessment and Management of Adult Patients With Atopic Dermatitis: A Consensus Document
Bibliographic record
Abstract
Background:Atopic dermatitis (AD) is a chronic, relapsing, and remitting inflammatory skin disease with complex pathophysiology, primarily driven by type 2 inflammation. Existing guidelines often do not reflect all current therapeutic options and guidance on the practical management of patients with AD is lacking.Objectives:To develop practical, up-to-date guidance on the assessment and management of adult patients with AD.Methods:An expert panel of 17 Canadian experts, including 16 dermatologists and 1 allergist, with extensive clinical experience managing moderate-to-severe AD reviewed the available literature from the past 5 years using a defined list of key search terms. This literature, along with clinical expertise and opinion, was used to draft concise, clinically relevant reviews of the current literature. Based on these reviews, experts developed and voted on recommendations and statements to reflect the practical management of adult patients with AD as a guide for health care providers in Canada and across the globe, using a prespecified agreement cutoff of 75%.Results:Eleven consensus statements were approved by the expert panel and reflected 4 key domains: pathophysiology, assessment, comorbidities, and treatment.Conclusions:These statements aim to provide a framework for the assessment and management of adult patients with AD and to guide health care providers in practically relevant aspects of patient management.
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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 | 0.076 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".