Bedside Intestinal Ultrasound Predicts Disease Severity and the Disease Distribution of Pediatric Patients With Inflammatory Bowel Disease: A Pilot Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Intestinal ultrasound (IUS) is a noninvasive tool to assess bowel inflammation. There is a paucity of data on its accuracy in pediatric patients. AIM: The aim of this study is to evaluate the diagnostic performance of bowel wall thickness (BWT) measured using IUS compared with endoscopic disease activity in children suspected of having inflammatory bowel disease (IBD). METHODS: We conducted a single-center cross-sectional pilot study of pediatric patients suspected to have previously undiagnosed IBD. Endoscopic inflammation was graded using segmental scores of the Simple Endoscopic Score for Crohn's Disease (SES-CD) and the Ulcerative Colitis Endoscopic Index of Severity (UCEIS) and classified as having healthy, mild, or moderate/severe disease activity. Association between BWT and endoscopic severity was assessed using the Kruskal-Wallis test. The diagnostic performance of BWT to detect active disease at endoscopy was evaluated using the area under the receiver operating characteristic curve; sensitivity and specificity were calculated. RESULTS: In all, 174 bowel segments in 33 children were assessed by IUS and ileocolonoscopy. An elevated median BWT was associated with increased bowel segment disease severity, classified by the SES-CD (P < .001) and the UCEIS (P < .01). Using a cutoff value of 1.9 mm, we found that the BWT had an area under the receiver operating characteristic curve of 0.743 (95% CI, 0.67-0.82), a sensitivity of 64% (95% CI, 53%-73%), and a specificity of 76% (95% CI, 65%-85%) to detect inflamed bowel. CONCLUSION: Increasing BWT is associated with increasing endoscopic activity in pediatric IBD. Our study suggests that the optimal BWT cutoff value for detecting active disease may be less than that seen in adults. Additional pediatric studies are needed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".