Burden of Disease and Unmet Needs in Atopic Dermatitis: Results from a Patient Survey
Bibliographic record
Abstract
Abstract: Background: Atopic dermatitis (AD) affects 2%–10% of adults worldwide. Occurrence and severity of symptoms and treatment success vary among patients. Objective: To determine disease severity, burden, and treatment use and satisfaction in adults with AD. Methods: An international internet-based survey was conducted (October 5–November 1, 2021) in participants with AD from Canada, France, Germany, Italy, Japan, Spain, the United Kingdom, and the United States. Results: Of 2005 AD patients surveyed, 92% had body surface area (BSA) involvement <10%. Itch was the most bothersome symptom; 48.5% of participants reported severe itch in the past week (Itch Numerical Rating Scale [NRS] 7–10; 45.9% for BSA <10%, 75.0% for BSA ≥10%). Most participants reported moderate or severe sleep disturbance in the past week (Sleep NRS 4–10; 67.1% for BSA <10%, 92.3% for BSA ≥10%). Itch was the top reason for participants' most recent health care provider visit; reducing itching was their top treatment goal. Topical therapies, which were most commonly used, resulted in low treatment satisfaction. Conclusions: Itch was the most bothersome AD symptom. Although clinical development has focused on improving skin lesions, improving itch is patients' top treatment goal. This survey highlights the need for systemic antipruritic therapies that could reduce itch in nonlesional and lesional skin.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".