Differentiation of Colonic Inflammatory Bowel Disease
Bibliographic record
Abstract
OBJECTIVES: Differentiation of Crohn disease (CD) from ulcerative colitis (UC) is challenging when inflammation is predominantly colonic. The paediatric inflammatory bowel disease (PIBD) classes algorithm was developed to bring consistency to labelling, but used physician-assigned diagnosis as the criterion standard. We aimed to reassess the PIBD classes using pathology of subsequently resected colon as the criterion standard. METHOD: Single-centre study of patients diagnosed with colonic IBD between 2002 and 2017 and subsequently treated with colectomy. Baseline pretreatment data were reviewed and the PIBD classes algorithm was independently applied by 2 reviewers to assign a label of UC/IBD-unclassified (IBD-U)/colonic-CD. Concordance between the algorithm-based, precolectomy clinical, and pathologic examination of resected colon diagnosis were assessed. Changes in diagnosis during postcolectomy follow-up were recorded. RESULTS: Sixty-two children underwent colectomy for medically refractory colonic IBD. Diagnosis based on pathologic review of resected colon CD:4;UC:56;IBDU:2. The clinical, PIBD classes algorithm, and colectomy diagnoses were concordant in 51 of 62 patients (81%, Fleiss kappa 0.48). Precolectomy clinical diagnosis was concordant with colectomy diagnosis in 58 of 62 patients (94%, weighted-kappa 0.65). The PIBD classes label was concordant with colectomy diagnosis in 51 of 62 patients (82%, weighted-kappa 0.38); resected colon pathology was typical of UC in 6 patients with PIBD classes label of IBD-U based on single class 2 feature and in 3 with PIBD classes label of CD based on single class 1 feature. CONCLUSIONS: Concordance of PIBD classes algorithm diagnosis applied before colectomy with a diagnostic label based on pathologic examination of a subsequently resected colon is only fair. Caution is needed in stringent application of colonic CD and IBD-U labels based on presence of single feature.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".