Supporting evidence and recommendations for the management of patients with systemic-eligible atopic dermatitis: A Canadian consensus document
Bibliographic record
Abstract
Background: Atopic dermatitis is a chronic, relapsing, and remitting inflammatory skin disease. Multiple systemic therapeutic options are available to treat atopic dermatitis. Objective: To provide evidence-based recommendations on the use of systemic therapies for atopic dermatitis in Canada that consider the nuances of the Canadian healthcare system and provide guidance for populations of clinical interest. Methods: A panel of 14 experts, including 11 dermatologists from Canada and 3 from the United States, reviewed available literature on systemic therapies for atopic dermatitis. The published evidence, along with clinical expertise and opinion, was used to draft a concise set of statements to guide healthcare providers in Canada on systemic treatment of atopic dermatitis. Results: During 3 rounds of virtual meetings with all 14 experts voting in all meetings, a total of 29 statements reached the 75% agreement required for consensus. Limitations: The consensus statements are based on expert opinions and consensus in the absence of evidence-based clinical research for patient outcomes. The statements represent considerations for patient management and not specific guidelines for patient treatment. Conclusion: The recommendations and statements provided serve to guide Canadian healthcare providers on the practical aspects of managing systemic-eligible patients with atopic dermatitis.
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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 | 0.077 | 0.197 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.012 | 0.006 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".