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 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 | 0.495 | 0.449 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.009 | 0.018 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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".