MR Enterography Scores Correlate with Degree of Mucosal Healing in Pediatric Crohn’s Disease: A Pilot Study
Bibliographic record
Abstract
Abstract Objectives MR enterography (MRE) Index of Activity (MaRIA) and Clermont are validated scores that correlate with Crohn’s disease (CD) activity; however, the Clermont score has not been validated to correlate with the degree of change in mucosal inflammation post induction treatment in children. This pilot study evaluated if MaRIA and Clermont scores can serve as surrogates to ileocolonoscopy for assessing interval change in mucosal inflammation in pediatric CD post-induction treatment. Methods Children with known or newly diagnosed ileocolonic CD starting or changing therapy underwent ileocolonoscopy, scored with simple endoscopic score for Crohn’s disease (SES-CD), and MRE on the same day at two time points (Week 0 and 12). Accuracy of global MaRIA and Clermont indices relative to ileocolonoscopy in detecting degree of post-treatment interval change in mucosal inflammation was assessed through correlational coefficients (r). Inter-reader agreement was calculated for imaging scores through intraclass correlation (ICC). Results Sixteen children (mean age 11.5 ± 2.8) were evaluated. Global MaRIA/Clermont correlated with SES-CD in detecting the degree of change in mucosal inflammation (r = 0.676 and r = 0.677, P < 0.005, respectively). Correlation for pooled timepoint assessments between SES-CD and global MaRIA/Clermont was moderate (r = 0.546, P < 0.001 and r = 0.582, P < 0.001, respectively). Inter-rater reliability for global MaRIA and Clermont was good (ICC = 0.809 and ICC = 0.768, respectively, P < 0.001). Conclusions MRE-based global scores correlate with endoscopic indices and may be used to monitor disease changes in children with CD undergoing induction treatment, which can advise the physician if treatment changes should be made.
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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 | 0.006 |
| 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".