Patient Preferences in the Treatment of Moderate-to-severe Atopic Dermatitis
Bibliographic record
Abstract
Atopic dermatitis is a chronic, inflammatory skin disease. A variety of systemic treatments are available for patients with moderate-to-severe atopic dermatitis. The efficacy, safety and administration profile of these treatments vary, and determining the optimal treatment strategy may require weighing the benefits and drawbacks of therapies with diverse characteristics. This study used an online discrete choice experiment survey to investigate treatment preferences among adults with atopic dermatitis from Denmark, France, the UK, or Canada. Participants were identified through existing online panels. The survey included questions regarding different treatment attributes, defined based on currently approved treatments for moderate to severe atopic dermatitis. Treatment preferences were measured as the relative importance of different treatment attributes. A total of 713 respondents met the inclusion criteria and completed the survey. The discrete choice experiment identified a significant preference for avoiding the risk of severe adverse events, and for oral pill every day compared with biweekly injections. The time to full effect was not rated as being important. These findings suggest that patients with moderate-to-severe atopic dermatitis prioritize safety as most important, followed by ease of administration in their treatment preferences, while time to full effect and monitoring requirements were the least important attributes.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".