Predictive testing for Inflammatory Bowel Diseases: The views of unaffected siblings
Bibliographic record
Abstract
Inflammatory bowel diseases (IBD) are chronic inflammatory conditions of the colon and small intestine with an incidence of around 1:10,000 in the United States. Both Crohn's disease (CD) and ulcerative colitis (UC) exhibit a multifactorial mode of inheritance with well recognized genetic, immunological, and environmental contributions to disease susceptibility. Although genetic testing for IBD is not currently utilized to predict disease, research into the genetic etiology of CD and UC suggests that such testing will likely become available. Siblings of those with IBD are the first degree relatives with the greatest risk to develop IBD. To investigate factors considered important in adolescents' and young adults' decisions to participate in predictive testing for IBD. Unaffected individuals aged 13 to 25 years with at least one sibling with IBD were invited to participate. Recruitment took place through the IBD clinics at The Hospital for Sick Children in Toronto, through online sources including Facebook and the Crohn's & Colitis Foundation of Canada, and through the Dana Children's Hospital in Tel Aviv. Participants were asked to complete a 15 minute online questionnaire developed from constructs of the Health Belief Model. Data analysis was performed on 130 questionnaires of which 70% of respondents were female and 30% were male. 88% of participants expressed interest in predictive testing for IBD and this did not significantly differ by gender or age of participant. This is consistent with previous studies looking at patients with IBD and their interest in genetic testing for themselves and for their children. The top two reasons cited in favor of testing included: to watch for early symptoms of the disease and to help science. However, 45% of individuals felt that a positive test result would make them feel anxious with females being significantly more likely to worry about developing IBD than males. To the best of our knowledge, this is the first study to date looking at interest in predictive testing for IBD amongst healthy individuals. We found a high level of interest to participate in predictive testing for IBD amongst those with an affected sibling. This data is important to consider in the development and implementation of testing for common, complex disorders.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".