Patients Prioritize a Low-volume Bowel Preparation in Colitis-associated Colorectal Cancer Surveillance: A Discrete Choice Experiment
Bibliographic record
Abstract
BACKGROUND: Patients with inflammatory bowel disease (IBD) undergo surveillance colonoscopies at fixed intervals to reduce the risk of colorectal cancer (CRC). Taking patients' preferences for determining surveillance strategies into account could improve adherence and patient satisfaction. This study aimed to determine patient preferences for CRC surveillance in IBD. METHODS: We conducted a web-based, multicenter, discrete choice experiment among adult IBD patients with an indication for surveillance. Individuals were repeatedly asked to choose between 3 hypothetical surveillance scenarios. The choice tasks were based on bowel preparation (0.3-4 L), CRC risk reduction (8% to 1%-6%), and interval (1-10 years). Attribute importance scores, trade-offs, and willingness to participate were calculated using a multinomial logit model. Latent class analysis was used to identify subgroups with similar preferences. RESULTS: In total, 310 of 386 sent out questionnaires were completed and included in the study. Bowel preparation was prioritized (attribute importance score 40.5%) over surveillance interval and CRC risk reduction (31.1% and 28.4%, respectively). Maximal CRC risk reduction, low-volume bowel preparation (0.3 L laxative with 2 L clear liquid) with 2-year surveillance was the most preferred combination. Three subgroups were identified: a "surveillance avoidant," "CRC risk avoidant," and "surveillance preferring" groups. Membership was correlated with age, educational level, perceived CRC risk, the burden of bowel preparation, and colonoscopies. CONCLUSIONS: Inflammatory bowel disease patients consider bowel preparation as the most important element in acceptance of CRC surveillance. Heterogeneity in preferences was explained by 3 latent subgroups. These findings may help to develop an individualized endoscopic surveillance strategy in IBD patients.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".