Canadian Consensus Guidelines for the Management of Atopic Dermatitis with Topical Therapies
Bibliographic record
Abstract
INTRODUCTION: Atopic dermatitis (AD) is a highly prevalent disease in Canada with significant patient burden. Treatment guidance for topical therapy (the mainstay of AD management), with particular consideration of emerging treatments, may further improve patient care. Here, we aim to provide healthcare professionals with AD treatment recommendations from the perspective of 10 Canadian dermatologists with expertise in managing AD. METHODS: The panel of dermatologists conducted a systematic literature review and leveraged their clinical experience to develop generally accepted principles, consensus statements, and a treatment algorithm using an iterative consensus process. RESULTS: The panel collectively developed six generally accepted principles, 10 consensus statements, and a treatment algorithm. The guidance notes that assessment of disease severity should encompass both physician-rated measures and patient-reported outcomes. Disease education, lifestyle-based strategies (e.g., trigger avoidance), and supportive measures (e.g., moisturizers) can help reduce signs and symptoms of AD. Choice of therapy should consider disease-, patient-, and treatment-related factors. Although topical corticosteroids (TCS) are often used as first-line treatment in AD, they should be limited to intermittent short-term use. Noncorticosteroid topical therapies (e.g., topical calcineurin inhibitors; topical phosphodiesterase-4 inhibitors; and topical Janus kinase inhibitors) can be used for widespread involvement of AD according to approved use. Once treatment goals are achieved, noncorticosteroid topical maintenance therapy should continue to prevent flares and reduce the need for TCS. CONCLUSION: Guidance reflecting the benefits and limitations of topical AD treatments in conjunction with patient understanding of treatment goals supports robust shared decision-making in the management of AD.
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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 | 0.027 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 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".