Predictive Prenatal Diagnosis for Infantile‐onset Inflammatory Bowel Disease Because of Interleukin‐10 Signalling Defects
Bibliographic record
Abstract
OBJECTIVES: Advances in genetic technologies provide opportunities for patient care and ethical challenges. Clinical care of patients with rare Mendelian disorders is often at the forefront of those developments. Whereas in classical polygenic inflammatory bowel disease (IBD), the predictive value of genetic variants is very low, predictive prenatal genetic diagnosis can inform families at high risk of severe genetic disorders. Patients with IL-10 signalling defects because of pathogenic variants in IL10RA, Il10RB, and IL10 develop severe infantile onset inflammatory bowel disease that is completely penetrant and has a high morbidity and substantial mortality despite treatment. METHODS: We performed a survey among tertiary specialist paediatric centers of 10 countries on the utilization of predictive prenatal genetic diagnosis in IL-10 signalling defects. We retrospectively report prenatal genetics in a series of 8 families. RESULTS: International variation in legislation, guidelines, expert opinion, as well as cultural and religious background of families and clinicians results in variable utilization of preimplantation and prenatal genetic testing for IL-10 signalling defects. Eleven referrals for prenatal diagnosis for IL-10 signalling defects were identified across 4 countries. We report on 8 families who underwent prenatal preimplantation monogenic testing after in vitro fertilization (n = 2) and/or by amniocentesis/chorion villus sampling (n = 6). A genetic diagnosis was established in 1 foetus and excluded in 7 foetuses (all IL10RA variants). CONCLUSIONS: Prenatal genetic testing for IL10R-defects is feasible, yet the legal and ethical considerations are complex and controversial. In some countries, predictive genetics for IL-10-related signalling defects is entering clinical practice.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".