Patient preferences for treatment attributes in moderate-to-severe atopic dermatitis: a discrete choice experiment
Bibliographic record
Abstract
Purpose: Evidence on treatment preferences of patients with moderate-to-severe atopic dermatitis (AD) in the United States (US) is limited and an assessment of treatment preferences in this group is warranted.Materials and methods: An online discrete choice experiment survey was conducted (June 2023) among US adults with self-reported moderate-to-severe AD or experience with systemic therapy who had inadequate response to topical treatments. Preference weights estimated from conditional logistic regression models were used to calculate willingness to trade off and attributes’ relative importance (RI).Results: Participants (N = 300; mean age: 45 years; 70% females; 52% systemic therapy experienced) preferred treatments with higher efficacy, lower risk of adverse events (AEs), and less frequent blood tests (p < .05). Treatment attributes, from high to low RI, were itch control (38%), risk of cancer (23%), risk of respiratory infections (18%), risk of heart problems (11%), sustained improvement in skin appearance (5%), blood test frequency (3%), and frequency and mode of administration (2%); together, AE attributes accounted for more than half of the RI.Conclusions: Participants preferred AD treatments that maximize itch control while minimizing AE risks, whereas mode of administration had little impact on preferences. Understanding patients’ preferences may help improve shared decision-making, potentially leading to enhanced patient satisfaction with treatment, increased engagement, and better clinical outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".