A171 MAGNETIC RESONANCE IMAGING IN CHILDREN WITH EARLY ONSET INFLAMMATORY BOWEL DISEASE- A RETROSPECTIVE COHORT STUDY
Bibliographic record
Abstract
Abstract Background Magnetic Resonance (MR) Imaging is the preferred imaging method in Inflammatory Bowel Disease (IBD) patients to investigate for small bowel disease. There are challenges in performing MR imaging in Early Onset IBD (EO-IBD) patients, and in particular in children with Very Early Onset IBD (VEO-IBD). These children often need a general anaesthetic which exposes them to adverse effects and preclude conventional luminal distention influencing the quality of the test. Therefore, the utility of MR imaging in this age group is questionable. Aims To assess the quality of MRI studies in VEO-IBD and EO-IBD patients and to compare the utility of this test between the two groups. Methods We retrospectively identified and reviewed IBD patients diagnosed under 10 years of age, between January 1999 and December 2011, from the British Columbia Children’s Hospital (BCCH) GI Division IBD database. Patients’ first diagnostic MRI results were recorded. Disease location and severity were documented according to the Paris classification. Results 124 patients were included in the cohort, 54 VEO-IBD and 70 EO-IBD patients (See Table 1). Median age at diagnosis was 6.46 (IQR 3.94–8.71), 65.32% males and 43.54% were diagnosed with Crohn’s disease. Overall, 52 patients underwent MRI, 17 (31.48%) in the VEO-IBD group and 35 (50%) in the EO-IBD group; median time from diagnosis to MRI was 3.02 years (IQR 1.08–5.83) for VEO-IBD and 0.44 years (IQR 0.07–1.58) for EO-IBD (p<0.001). In the EO-IBD group there was a significantly higher percentage of patients with MRI findings than in the VEO-IBD group, 23 (67.31%) and 5 (29.41%) respectively (p=0.014). Only one patient in the VEO-IBD group had a disease characteristic identified by MR imaging that could not be diagnosed by endoscopy (small bowel disease). Conclusions The diagnostic yield of MRI in children with VEO-IBD appears to be quite limited but requires further study. Funding Agencies None
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".