405 Understanding patient experience and factors influencing patient preference in the treatment of moderate-to-severe atopic dermatitis through in-depth qualitative patient interviews
Bibliographic record
Abstract
Abstract Atopic dermatitis (AD) can enormously affect the lives of patients and their caregivers. Treatments for moderate-to-severe AD include phototherapy, topical and systemic corticosteroids, other immunosuppressive agents and biologic therapies. These treatments offer varied levels of efficacy, safety, response times and modes of administration. Patient preferences for these treatment options and attributes influencing their treatment choice are not well characterized. This qualitative interview-based study evaluated treatment attributes important to adult patients with moderate-to-severe AD providing a basis for a future quantitative discrete choice experiment (DCE). Semi-structured interviews were conducted in adults (≥18 years) recruited via a panel of geographically and demographically diverse patients with AD in the USA. Inclusion required (i) diagnosis of AD for at least one year, (ii) inadequate response to prior topical therapy and (iii) self-reported moderate-to-severe AD or experience with systemic therapy. One-on-one, in-depth 60-min phone interviews were conducted by a trained interviewer in AD real-word evidence studies. Subjects with experience with systemic therapy for AD were queried concerning their involvement in initiating, logistics of and general experience with their most recent systemic treatment. Finally, participants were asked to rate the importance (on a 1–5 scale with 5 being the most important) of 17 attributes that could play a role in their treatment decision-making; these attributes were identified from a targeted literature review and expert dermatologist clinical input. Attributes included treatment efficacy, adverse events and treatment logistics. Interviews are ongoing, and results will be ready and presented in the poster upon acceptance. Findings from the interviews will shed light on recent patient experience with treatments for moderate-to-severe AD and elucidate the treatment attributes that most impact patient treatment preferences. These results will guide the design of a subsequent DCE survey, which will help quantify the relative importance of the treatment attributes identified herein.
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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 | 0.026 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".