Combining treat‐to‐target principles and shared decision‐making: International expert consensus‐based recommendations with a novel concept for minimal disease activity criteria in atopic dermatitis
Bibliographic record
Abstract
BACKGROUND: Current treat-to-target recommendations for atopic dermatitis (AD) may not include high enough treatment targets and do not fully consider patient needs. OBJECTIVE: To develop recommendations for optimized AD management, including disease severity assessments, treatment goals and targets, and guidance for treatment escalation/modification. METHODS: An international group of expert dermatologists drafted a series of recommendations for AD management using insights from a global patient study and 87 expert dermatologists from 44 countries. Experts voted on recommendations using a modified eDelphi voting process. RESULTS: The Aiming High in Eczema/Atopic Dermatitis (AHEAD) recommendations establish a novel approach to AD management, incorporating shared decision-making and a concept for minimal disease activity (MDA). Consensus (≥70% agreement) was reached for all recommendations in 1 round of voting; strong consensus (≥90% agreement) was reached for 30/34 recommendations. In the AHEAD approach, patients select their most troublesome AD feature(s); the clinician chooses a corresponding patient-reported severity measure and objective severity measure. Treatment targets are chosen from a list of 'moderate' and 'optimal' targets, with achievement of 'optimal' targets defined as MDA. CONCLUSIONS: Patient and expert insights led to the development of AHEAD recommendations, which establish a novel approach to AD management. Patients were not involved in the eDelphi voting process used to generate consensus on each recommendation. However, patient perspectives were captured in a global, qualitative patient research study that was considered by the experts in their initial drafting of the recommendations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".