Adolescent and caregiver preferences for juvenile idiopathic arthritis treatment: a discrete-choice experiment
Bibliographic record
Abstract
BACKGROUND: This study aimed to elicit and quantify preferences for treatments for juvenile idiopathic arthritis (JIA). METHODS: We conducted a discrete-choice experiment among adolescents with JIA in the United States (US) (n = 197) and United Kingdom (UK) (n = 100) and caregivers of children with JIA in the US (n = 207) and UK (n = 200). In a series of questions, respondents chose between experimentally designed profiles for hypothetical JIA treatments that varied in efficacy (symptom control; time until next flare-up), side effects (stomachache, nausea, and vomiting; headaches), mode and frequency of administration, and the need for combination therapy. Using a random-parameters logit model, we estimated preference weights for these attributes, from which we derived their conditional relative importance. RESULTS: On average, respondents preferred greater symptom control; greater time until the next flare-up; less stomachache, nausea, and vomiting; and fewer headaches. However, adolescents and caregivers in the US were generally indifferent across varying modes and frequencies of administration. UK adolescents and caregivers preferred tablets, syrup, or injections to intravenous infusions. US and UK adolescents were indifferent between treatment with monotherapy or combination therapy; caregivers in the UK preferred treatment with combination therapy to monotherapy. Subgroup analysis showed preference heterogeneity across characteristics including gender, treatment experience, and symptom experience in both adolescents and caregivers. CONCLUSIONS: Improved symptom control, prolonged time to next flare-up, and avoidance of adverse events such as headache, stomachache, nausea, and vomiting are desirable characteristics of treatment regimens for adolescents with JIA and their caregivers.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".