Reliability and Validity of Mesenteric Fat Assessment by Intestinal Ultrasound in Pediatric Crohn’s Disease Using the Chicago Mesenteric Fat Index
Bibliographic record
Abstract
BACKGROUND: Intestinal ultrasound (IUS) provides a noninvasive means of assessing Crohn's disease (CD), including visualization of mesenteric fat (MF) wrapping. Reliability of MF assessment and correlation with disease activity biomarkers in children is unknown. This study assessed the interrater reliability (IRR) of a binary assessment and a novel semi-quantitative index for grading MF wrapping using IUS (Chicago Mesenteric Fat Index [CMFI]) and correlation with disease activity biomarkers in pediatric patients with CD. METHODS: Children (≤18 years of age) with ileal CD who underwent IUS at 2 centers were enrolled. Three expert sonographers independently graded MF as present/absent and by the CMFI. IRR was calculated using Fleiss' kappa coefficient. Correlations between MF and clinical characteristics, inflammatory markers, and IUS data were calculated. RESULTS: Eighty IUS exams in 67 patients were included. The IRR was substantial for binary MF (κ = 0.744) and CMFI (κ = 0.618). Increasing CMFI grade was associated with bowel wall thickness (P < .001; odds ratio [OR], 16.35; 95% confidence interval [CI], 5.74-46.58), presence of ileal stricture (P < .001; OR, 30.32; 95% CI, 5.74-160.15), and presence of hyperemia (P = .001; OR, 6.59; 95% CI, 2.27-19.09). CONCLUSION: Assessment of MF on IUS is reproducible and reliable in pediatric CD. The CMFI can be used as a biomarker that mirrors biochemical and sonographic indicators of pediatric CD activity.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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".