Magnetic Resonance Imaging to Distinguish the Type and Severity of Pediatric Inflammatory Bowel Diseases
Bibliographic record
Abstract
BACKGROUND: The distinction between ulcerative colitis and Crohn's disease is important, because treatment options and clinical course may vary. Magnetic resonance imaging (MRI) allows noninvasive transmural assessment of the intestine and may facilitate differentiation of ulcerative colitis from Crohn's disease. The objective of this prospective study was to determine whether MRI differentiates Crohn's disease from ulcerative colitis in children as effectively as colonoscopy with mucosal biopsies. METHODS: Fifteen patients underwent colonoscopy with biopsies followed by abdominal MRI. The MRI diagnosis, determined by two radiologists independently completing a standardized form was compared with the gastroenterologic diagnosis. RESULTS: After colonoscopy and review of histology, Crohn's disease was diagnosed in nine patients, ulcerative colitis in five, and indeterminate colitis in one, who was excluded from study. Agreement of the MRI diagnosis with the gastroenterologic diagnosis was 4 of 4 (100%) for ulcerative colitis, 4 of 10 (40%) for Crohn's disease considering both radiologists, and 5 of 10 (50%) for Crohn's disease for each radiologist individually. Percentage of enhancement by MRI did not correlate with the severity of inflammation determined at endoscopy among the patients with Crohn's disease (r = -0.3, P = 0.366). There was agreement on severity of inflammation in three of four patients with ulcerative colitis. CONCLUSIONS: Current MRI interpretation of inflammatory bowel disease did not adequately recognize Crohn's disease in children. Therefore, colonoscopy with biopsy remains the most accurate tool for determining the type and severity of inflammatory bowel disease in children and adolescents.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".