Correlation of Point‐of‐Care Intestinal Ultrasound With Endoscopic Disease Severity in Crohn's Disease
Bibliographic record
Abstract
Background: Intestinal ultrasound is increasingly being used in the management of patients with inflammatory bowel disease (IBD). We aim to evaluate the accuracy of intestinal ultrasound in a point-of-care setting in India and compare it with ileo-colonoscopy, the current gold standard. Methods: In this prospective observational study, consecutive patients with a diagnosis of Crohn's disease seen at initial presentation or follow-up were included. At the out-patient visit, clinical severity and biomarkers were documented, and point-of-care intestinal ultrasound was performed. Colonoscopy was performed within 1 week for all patients. Results: A total of 254 patients with Crohn's disease who underwent ileo-colonoscopy were included in the study. The mean bowel wall thickness (BWT) in patients with normal endoscopy (SES-CD < 2) was 2.09 mm. The mean BWT in patients with mild, moderate to severe, and severe disease activity was 4.7, 5.23, and 6.25 mm, respectively. A threshold of 3.4 mm had the best ability to predict the presence of endoscopic disease activity, with a sensitivity of 93%, specificity of 90%, and AUC of 0.96 in this study. The presence of color Doppler signals had a sensitivity of 96.3% and specificity of 91.2% to predict the presence of endoscopic disease activity. Inflammatory fat and bowel wall stratification (BWS) had a higher odds ratio to predict more severe disease. Conclusion: Point-of-care-intestinal ultrasound has good correlation with ileocolonoscopy and can be utilized to assess and monitor disease activity, which should facilitate real-time decision making in the management of patients with IBD.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".