Patient and First‐Degree Relatives Perceptions About Prediction and Prevention of Inflammatory Bowel Disease—A Multinational Survey
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: While significant advances have been made in identifying biomarkers for predicting inflammatory bowel disease (IBD) onset, little is known about the willingness of at-risk individuals to undergo predictive testing and preventive interventions. This study aimed to assess acceptance of predictive tests and preventive interventions among individuals at risk of inflammatory bowel disease and identify factors influencing their decisions. METHODS: An anonymized electronic survey was distributed to parents of children at risk of inflammatory bowel disease and first-degree relatives (FDRs) of IBD patients via clinicians and patient associations. The survey assessed acceptance of predictive tests, preventive interventions, and influencing variables. RESULTS: A total of 1327 participants (74% women, mean age 42 ± 18 years) from 66 countries responded to the survey. Of these, 88% were parents of children at risk, and 12% were FDRs. Eighty-five percent were willing to embark in predictive testing, preferring blood analysis (91%), stool tests (89%), saliva tests (78%) or intestinal ultrasound (67%). Lower perceived IBD impact and higher disease knowledge reduced the odds of test acceptance. Preventive interventions were accepted by 98%, with dietary changes (85%), physical exercise (81%), and probiotics (71%) being the preferred. Acceptance of oral (38%) or intravenous/subcutaneous immunosuppressive treatments (32%) depended on their efficacy, difficulty, and risks. CONCLUSION: Most respondents preferred minimally invasive predictive tests and non-pharmacological preventive measures, although more than one-third would be willing to undergo immunosuppressive medications to prevent disease onset. Disease knowledge and quality-of-life perceptions influenced preferences. This data provides important information for the development of IBD prediction and prevention strategies.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".