Diagnostic Yield of Next-generation Sequencing in Very Early-onset Inflammatory Bowel Diseases: A Multicentre Study
Bibliographic record
Abstract
BACKGROUND AND AIMS: An expanding number of monogenic defects have been identified as causative of severe forms of very early-onset inflammatory bowel diseases [VEO-IBD]. The present study aimed at defining how next-generation sequencing [NGS] methods can be used to improve identification of known molecular diagnosis and to adapt treatment. METHODS: A total of 207 children were recruited in 45 paediatric centres through an international collaborative network [ESPGHAN GENIUS working group] with a clinical presentation of severe VEO-IBD [n = 185] or an anamnesis suggestive of a monogenic disorder [n = 22]. Patients were divided at inclusion into three phenotypic subsets: predominantly small bowel inflammation, colitis with perianal lesions, and colitis only. Methods to obtain molecular diagnosis included functional tests followed by specific Sanger sequencing, custom-made targeted NGS, and in selected cases whole exome sequencing [WES] of parents-child trios. Genetic findings were validated clinically and/or functionally. RESULTS: Molecular diagnosis was achieved in 66/207 children [32%]: 61% with small bowel inflammation, 39% with colitis and perianal lesions, and 18% with colitis only. Targeted NGS pinpointed gene mutations causative of atypical presentations, and identified large exonic copy number variations previously missed by WES. CONCLUSIONS: Our results lead us to propose an optimised diagnostic strategy to identify known monogenic causes of severe IBD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".