Crohn’s Disease Patient Experiences and Preferences With Disease Monitoring: An International Qualitative Study
Bibliographic record
Abstract
Background: Strategies incorporating objective disease monitoring in Crohn's disease (CD), beyond clinical symptoms are important to improve patient outcomes. Little evidence exists to explore patient understanding of CD treatment goals, nor preferences and experiences with monitoring options. This qualitative study aimed to explore patient experiences and preferences of CD monitoring to inform monitoring strategies, improve patient engagement, and optimize a patient-centered approach to care. Methods: This study used a patient-oriented, qualitative descriptive design. Convenience and snowball sampling were used to recruit adult participants diagnosed with CD who had experience with at least 2 types of disease monitoring. Online focus groups were conducted and data were analyzed using thematic analysis. Results: This international study included 37 participants from Australia, Canada, United Kingdom, and the United States. Overall, participants preferred more noninvasive types of monitoring [eg, intestinal ultrasound (IUS)] but were willing to undergo more invasive monitoring (eg, colonoscopy) if required. To improve disease monitoring, participants wanted increased access to IUS, establishment of a patient-centered interdisciplinary team and access to information and self-testing. Participants identified challenges with communication between patients and providers and stressed the importance of participating in shared decision making and being equal team members in their care. Conclusions: It is imperative to incorporate patient-driven preferences into how we can best structure monitoring strategies, to ensure equitable access to those preferred modalities and embrace a shared decision-making approach to disease management in CD.
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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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".