Application of a pattern‐based approach to histological diagnosis in very early onset IBD (VEO‐IBD) in a multicentric cohort of children with emphasis on monogenic disease with IBD‐like morphology
Bibliographic record
Abstract
AIMS: Very early-onset inflammatory bowel disease (VEO-IBD) is a clinical umbrella term referring to IBD-like symptoms arising in children before the age of 6 years, encompassing both 'pure' IBD, such as ulcerative colitis (UC) and Crohn's disease (CD) and monogenic diseases (MDs), the latter often involving genes associated with primary immunodeficiencies. Moreover, histological features in gastrointestinal (GI) biopsies in MD can also have IBD-like morphology, making differential diagnosis difficult. Correct diagnosis is fundamental, as MDs show a more severe clinical course and their inadequate/untimely recognition leads to inappropriate therapy. METHODS AND RESULTS: Biopsy samples from the lower and upper GI tract of 93 clinically diagnosed VEO-IBD children were retrospectively selected in a multicentre cohort and histologically re-evaluated by 10 pathologists blinded to clinical information. Each case was classified according to morphological patterns, including UC-like; CD-like; enterocolitis-like; apoptotic; eosinophil-rich; and IBD-unclassified (IBD-U). Nine (69%) MD children showed IBD-like morphology; only the IBD-U pattern correlated with MD diagnosis (P = 0.02) (available in 64 cases: 51 non-MD, true early-onset IBD/other; 13 MD cases). MD patients showed earlier GI symptom onset (18.7 versus 26.9 months) and were sent to endoscopy earlier (22 versus 37 months), these differences were statistically significant (P < 0.05). Upper GI histology was informative in 37 biopsies. CONCLUSIONS: The diagnosis of the underlying cause of VEO-IBD requires a multidisciplinary setting, and pathology, while being one of the fundamental puzzle pieces, is often difficult to interpret. A pattern-based histological approach is therefore suggested, thus aiding the pathologist in VEO-IBD reporting and multidisciplinary discussion.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".