Magnetic Resonance Enterography Cannot Replace Upper Endoscopy in Pediatric Crohn Disease
Bibliographic record
Abstract
OBJECTIVES: Although magnetic resonance enterography (MRE) can accurately reflect ileal inflammation in pediatric Crohn disease (CD), there are no pediatric data on the accuracy of MRE to detect upper gastrointestinal tract (UGI) lesions. We aimed to compare MRE and esophagogastroduodenoscopy (EGD) in detecting the spectrum and severity of UGI disease in children. METHODS: This is an ancillary study of the prospective multi-center ImageKids study focusing on pediatric MRE. EGD was performed within 2 weeks of MRE (at disease onset or thereafter) and explicitly scored by SES-CD modified for the UGI and physician global assessment. Local and central radiologists scored the UGI region of the MRE blinded to the EGD. Accuracy of MRE compared with EGD was examined using correlational coefficients (r) and area under receiver operating characteristic curves (AUC). RESULTS: One hundred and eighty-eight patients were reviewed (mean age 14 ± 1 years, 103 [55%] boys); 66 of 188 (35%) children had macroscopic ulcerations on EGD (esophagus, 13 [7%]; stomach, 34 [18%]; duodenum, 45 [24%]). Most children had aphthous ulcers, but 10 (5%) had larger ulcers (stomach, 2 [1%]; duodenum, 8 [4%]). There was no agreement between local and central radiologists on the presence or absence of UGI inflammation on MRE (Kappa = -0.02, P = 0.71). EGD findings were not accurately detected by MRE, read locally or centrally (r = -0.03 to 0.11, P = 0.18-0.88; AUC = 0.47-0.55, P = 0.53-1.00).No fistulae or narrowings were identified on either EGD or MRE. CONCLUSIONS: MRE cannot reliably assess the UGI in pediatric CD and cannot replace EGD for this purpose.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".