Advanced Understanding of Monogenic Inflammatory Bowel Disease
Bibliographic record
Abstract
Inflammatory bowel disease (IBD) is a group of chronic disorders that cause relapsing inflammation in the gastrointestinal tract and comprise three major subgroups of Crohn's disease (CD), ulcerative colitis (UC), and IBD-unclassified (IBDU). Recent advances in genomic technologies have furthered our understanding of IBD pathogenesis. It includes differentiation rare monogenic disorders exhibiting IBD and IBD-like inflammation (monogenic IBD) from patients with the common polygenic form of IBD. Several novel genes responsible for monogenic IBD have been elucidated, and the number of reports has increased due to advancements in molecular functional analysis. Identification of these pathogenic genetic mutations has helped in elucidating the details of the immune response associated with gastrointestinal inflammation and in providing individualized treatments for patients with severe IBD that is often unresponsive to conventional therapy. The majority of monogenic IBD studies have focused on young children diagnosed <6 years of age (very early-onset IBD); however, a recent study revealed high prevalence of monogenic IBD in older children aged >6 years of age as well. Meanwhile, although patients with monogenic IBD generally show co-morbidities and/or extraintestinal manifestation at the time of diagnosis, cases of IBD developing as the initial symptom with unremarkable prodromal symptoms have been reported. It is crucial that the physicians properly match genetic analytical data with clinical diagnosis and/or differential diagnosis. In this review, we summarize the essential clues that may physicians make a correct diagnosis of monogenic disease, including classification, prevalence and clinical phenotype based on available literatures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".