Inflammatory bowel disease patients prioritize mucosal healing, symptom control, and pain when choosing therapies: results of a prospective cross-sectional willingness-to-pay study
Bibliographic record
Abstract
BACKGROUND: Given the large armamentarium of therapies for inflammatory bowel disease (IBD), physicians cannot fully describe all treatments to patients and, therefore, make assumptions regarding treatment attributes communicated to patients. This study aimed to assess out-of-pocket willingness-to-pay that IBD patients allocate to treatment attributes. METHODS: Adult patients receiving therapy for IBD were invited to access a cross-sectional web-based discrete-choice experiment (May 22-August 31, 2015) that presented paired medication scenarios with varying efficacy, safety, and administration parameters. Preference weights and willingness-to-pay for each attribute level were assessed by a hierarchical Bayes method including a multinomial logit model. RESULTS: A total of 586 IBD patients were included, 404 (68.9%) with Crohn's disease and 182 (31.1%) with ulcerative colitis. Genders were evenly distributed; the majority of patients (70.1%) were 50 years or younger and had postsecondary education (75.4%), while the median health status was 7 (Likert scale: 1 [poor] - 10 [perfect]). Regarding relative preference-weight estimates, for the average respondent, reducing pain during administration, mucosal healing, and symptom relief were the highest-ranking attributes. Conversely, infusion reactions and risk of hospitalization or surgery were the lowest-ranking attributes. In multivariate analysis, patient sociodemographics did not affect the rank order of attributes although small differences were observed between asymptomatic and symptomatic patients in the previous year. CONCLUSION: This study has important implications related to understanding patient preferences and designing patient-centered strategies. IBD patients prioritize treatments with low administration pain. Additionally, these results concur with treatment guidelines emphasizing patients' preference for mucosal healing and symptom control.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".