Patient preferences for food allergy treatments in the United States: a discrete choice experiment
Bibliographic record
Abstract
Objective To estimate preferences of adolescents and caregivers of children with food allergies (FA) for food allergy for attributes for treatments intended to prevent exposure-induced severe reactions and to examine how these vary by clinical and demographic factors.Methods A discrete choice experiment (DCE) was conducted between May and June 2024 among US-resident individuals, aged 13–17 years or caregivers of a child with FA aged ≤12 years, self-reporting physician-diagnosed FA. The survey comprised treatment-attribute focused DCE choice sets; the Intolerance of Uncertainty-12 Scale (IUS-12); the Food Allergy Independent Measure (FAIM); and clinical/demographic questions. Conditional logistic regression analyses were conducted overall and among subgroups and presented as odds ratios (ORs) with 95% confidence intervals (CIs). Relative importance of attributes was also calculated.Results Participants were adolescents (n = 73, mean age 15.9 years) and caregivers (n = 228, mean age 37.1 years). Overall, treatment attributes associated with statistically significant odds for preferring a treatment included a 95% relative reduction in moderate-to-severe allergic reaction risk versus no reduction (p < 0.001); oral administration over subcutaneous (p < 0.001); administration in a home versus clinical setting (p < 0.05); less frequent versus daily administration (every two weeks: p < 0.001; every four weeks: p < 0.001). Respondents were disinclined to prefer treatment attributes when the risk of gastrointestinal symptoms (p < 0.001) or anaphylaxis (p < 0.001) increased. Efficacy (75% decrease in relative risk of a moderate-to-severe allergic reaction) was the top ranked attribute [OR = 1.61 (1.49, 1.75)].Conclusion Overall, respondents indicated significant preferences for more efficacious, safer, and convenient (oral, at-home, and less frequent) treatments. Treatment efficacy was the most highly ranked treatment feature.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".