Patient preferences for active ulcerative colitis treatments and fecal microbiota transplantation
Bibliographic record
Abstract
Background: Fecal microbiota transplantation (FMT) is a promising treatment for active ulcerative colitis (UC). Understanding patient preferences can identify treatment features that may impact treatment decisions, improve shared decision-making, and contribute to patient-centered care, which is especially important in the context of novel treatments like FMT. Objectives: We aimed to quantify preferences for active UC treatments, specifically FMT and biologics, and identify patient characteristics associated with different preference patterns. Design: This is a cross-sectional survey study. Methods: We administered a discrete choice experiment (DCE) survey to elicit preferences in a sample of Canadian adults with UC. DCE data were analyzed using a main-effects mixed logit model and used to predict uptake of hypothetical scenarios reflecting alternative combinations of treatment features. Latent class modeling identified heterogeneity in patient preference patterns. Results: = 201) mean age was 47.1 years (SD: 14.5 years), 58% were female, and most (84%) had at least some post-secondary education. Almost half were willing to undergo FMT. When considering treatments for active UC, the most important attributes were chance of remission and severity of rare unknown side effects. All else equal, participants were most likely to uptake treatment that involves oral capsules/pills. Participants in the class with the highest utility for chance of remission were younger, had more severe disease, and 58% indicated that they would be willing to undergo FMT. Conclusion: We identified characteristics of UC patients who are more likely to be interested in FMT using preference elicitation methods. Patient-centered care can be enhanced by knowing which patients are more likely to be interested in FMT, potentially improving satisfaction with and adherence to treatments for active UC to maximize the effectiveness of treatment while considering heterogeneity in patient preferences.
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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) | 0.000 | 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".