Treat‐to‐target in dermatology: A scoping review and International Eczema Council survey on the approach in atopic dermatitis
Bibliographic record
Abstract
Treat-to-target (T2T) is a pragmatic therapeutic strategy being gradually introduced into dermatology after adoption in several other clinical areas. Atopic dermatitis (AD), one of the most common inflammatory skin diseases, may also benefit from this structured and practical therapeutic approach. We aimed to evaluate existing data regarding the T2T approach in dermatology, with a specific focus on AD, as well as the views of International Eczema Council (IEC) members on the potential application of a T2T approach to AD management. To do so, we systematically searched for peer-reviewed publications on the T2T approach for any skin disease in the PubMed and Scopus databases up to February 2022 and conducted a survey among IEC members regarding various components to potentially include in a T2T approach in AD. We identified 21 relevant T2T-related reports in dermatology, of which 14 were related to psoriasis, five to AD, one for juvenile dermatomyositis and one for urticaria. In the IEC member survey, respondents proposed treatable traits (with itch, disease severity and sleep problems getting the highest scores), relevant comorbidities (with asthma being selected most commonly, followed by anxiety and depression in adults), recommended specialists that should define the approach in AD (dermatologists, allergists and primary care physicians were most commonly selected in adults), and applicable assessment tools (both physician- and patient-reported), in both adult and paediatric patients, for potential future utilization of the T2T approach in AD. In conclusion, while the T2T approach may become a useful tool to simplify therapeutic goals and AD management, its foundation in AD is only starting to build. A multidisciplinary approach, including a wide range of stakeholders, including patients, is needed to further define the essential components needed to utilize T2T in 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 | 0.019 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.030 | 0.036 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".