Perception and Experience of Biologic Therapy in Atopic Dermatitis: A Qualitative Focus Group Study of Physicians and Patients in Europe and Canada
Bibliographic record
Abstract
INTRODUCTION: The Biologics in Atopic Dermatitis: Experiences & Learnings (BADEL) project aims to improve real-life understanding of how, where, and when biologics can play a role in the treatment of atopic dermatitis (AD) from the perspective of healthcare professionals (HCPs) and patients. METHODS: Individual experiences of 24 patients with moderate-to-severe AD and who had been treated with biologic therapy (dupilumab) for ≥ 3-6 months, and 20 HCPs with a sub-specialty interest in AD were collected by means of focus groups held in Canada, Germany, France, Italy and the United Kingdom. Dupilumab was the only biologic therapy available at the time of the study. RESULTS: Most patients had suffered from AD for many years, particularly from itch and psychosocial issues, with AD negatively impacting all aspects of their life. They had experienced a long treatment journey and seen many dermatologists, enduring treatment delays and failures. They had been prescribed various therapies without long-term success. Biologics provided symptom improvement, offering many patients a near-normal quality of life. Side effects, especially conjunctivitis, were the greatest drawback, and there were a few issues with incomplete or unreliable efficacy. HCPs agreed that biologic therapy for AD in the majority of patients demonstrated rapid onset, good efficacy and tolerability, and are a viable option in patients who had exhausted all other treatment options. However, those patients who failed to sufficiently respond or developed intolerable adverse effects, particularly ocular symptoms, require alternative therapeutic options. CONCLUSION: Biologics can provide a near-normal quality of life for many patients with AD. Patients with AD who have failed conventional therapies should be offered all such novel therapies. Education and good patient-HCP communication will enable patients to manage their disease and treatment expectations. Patients and HCPs alike eagerly await alternative targeted therapies, which will offer greater choice and flexibility.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".