A87 DEVELOPMENT AND VALIDATION OF DIAGNOSTIC CRITERIA FOR IBD WITH AN EMPHASIS ON IBD-UCLASSIFIED IN CHILDREN: A MULTICENTER STUDY FROM THE PEDIATRIC IBD PORTO GROUP OF ESPGHAN
Bibliographic record
Abstract
The revised Porto criteria identify subtypes of pediatric inflammatory bowel diseases: ulcerative colitis (UC), atypical UC, Inflammatory Bowel Disease Unclassified (IBDU), and Crohn’s disease (CD). In continuation of the Porto criteria, we aimed to further derive and validate criteria for standardizing the diagnosis of the IBD subtypes with an emphasis on IBDU, the least well defined subtype. This was a multicenter retrospective longitudinal study from 23 centers affiliated with the Porto-group of ESPGHAN. Both a hypothesis driven judgmental approach and mathematical CART modeling were utilized for creating a diagnostic algorithm. Since jejunal and ileal inflammation is easily recognized as CD, we focused here on colitis phenotype. 749 IBD children were enrolled- 236 (32%) Crohn’s colitis (CD), 272 (36%) ulcerative colitis (UC) and 241 (32%(IBDU (age 10.9 ± 3.6 years) with a median follow-up of 2.8 years (IQR 1.7–4.3). A set of 23 features were clustered in 3 classes according to their frequency in UC: 6 class-1 (0% prevalence in UC), 12 class-2 (<5% prevalence) and 5 class-3 (5–10% prevalence). According to the algorithm, UC should be diagnosed if no features exist in the three classes. Different combinations of the features classify atypical UC, IBDU and CD. The algorithm differentiated UC from CD and IBDU with 80% sensitivity (95% CI (71–88)) and 84% specificity (95% CI (77–89)), and CD from IBDU and UC with 78% sensitivity (95% CI (67–87)) and 94% specificity (95% CI (89–97)). The validated algorithm can adequately classify children with IBD into CD, UC and IBDU. None
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".