Skin Allergy Research Society and Society for Eczema Studies Joint Task Force Guidelines of Care for Management of Atopic Dermatitis for Adults, Children, and Special Populations in India: An Evidence-Based Review and an Expert Consensus
Bibliographic record
Abstract
Atopic dermatitis (AD) is a chronic inflammatory skin disease with significant morbidity. Recognising the need for region-specific guidance, the Skin Allergy Research Society and Society for Eczema Studies have collaborated to develop updated, evidence-based guidelines tailored to the Indian context. These guidelines address AD management across all age groups, special populations while considering local epidemiology, healthcare infrastructure, and treatment accessibility. A structured Delphi consensus process was conducted among 23 dermatology experts over 3 months through virtual and in-person meetings. Literature from MEDLINE, Cochrane, and Google Scholar was systematically reviewed, and the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) approach was used to assess evidence quality. Clinical recommendations were refined through multiple voting rounds, leading to consensus statements. Recommendations are based on an extensive literature review up to December 2024. This document updates the 2019 Skin Allergy Society guidelines, reinforcing global recommendations while allowing local adaptability. These guidelines provide updated recommendations for topical, systemic, phototherapy, and biologic therapies in AD. Key advancements include the introduction of topical crisaborole and JAK inhibitors for mild to moderate AD, along with a focus on emerging systemic therapies, such as biologics and systemic JAK inhibitors. In the Indian context, the guidelines define the roles of dupilumab and abrocitinib while also addressing the off-label use of tofacitinib and baricitinib in resource-limited settings. Specific recommendations are provided for children, elderly patients, and pregnant women, emphasising safety considerations for systemic and biologic therapies. These guidelines align with global AD management while incorporating India-specific adaptations based on epidemiology, accessibility, and affordability. They serve as a key reference for dermatologists, pediatricians, and general practitioners in India and other resource-limited settings. Though tailored for India, they are also relevant to dermatologists in developing countries, guiding treatment selection based on disease patterns, environmental factors, and medication availability.
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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 | 0.031 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.019 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".