Clinical Genomics for the Diagnosis of Monogenic Forms of Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND: It is important to identify patients with monogenic IBD as management may differ from classical IBD. In this position statement we formulate recommendations for the use of genomics in evaluating potential monogenic causes of IBD across age groups. METHODS: The consensus included paediatric IBD specialists from the Paediatric IBD Porto group of the European Society of Paediatric Gastroenterology, Hepatology and Nutrition (ESPGHAN) and specialists from several monogenic IBD research consortia. We defined key topics and performed a systematic literature review to cover indications, technologies (targeted panel, exome and genome sequencing), gene panel setup, cost-effectiveness of genetic screening, and requirements for the clinical care setting. We developed recommendations that were voted upon by all authors and Porto group members (32 voting specialists). RESULTS: We recommend next-generation DNA-sequencing technologies to diagnose monogenic causes of IBD in routine clinical practice embedded in a setting of multidisciplinary patient care. Routine genetic screening is not recommended for all IBD patients. Genetic testing should be considered depending on age of IBD-onset (infantile IBD, very early-onset IBD, paediatric or young adult IBD), and further criteria, such as family history, relevant comorbidities, and extraintestinal manifestations. Genetic testing is also recommended in advance of hematopoietic stem cell transplantation. We developed a diagnostic algorithm that includes a gene panel of 75 monogenic IBD genes. Considerations are provided also for low resource countries. CONCLUSIONS: Genomic technologies should be considered an integral part of patient care to investigate patients at risk for monogenic forms of IBD.
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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 | 0.028 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".