Impact of Intestinal Ultrasound on Classification and Management of Crohn’s Disease Patients with Inconclusive Colonoscopy
Bibliographic record
Abstract
Background and Aims. We aim to evaluate the benefit of ultrasound in the assessment of Crohn's disease and to demonstrate its potential contribution to disease management. Methods. We conduct a retrospective review of adult patients with Crohn's disease examined with sonography and colonoscopy within 30 days. Study patients were identified in whom colonoscopy did not access a pathological segment, detected and evaluated by ultrasonography. Changes in management were predominantly attributed to ultrasound in those cases where the diseased segment was not assessed on endoscopy. Results. From 115 patients with temporally related ileocolonoscopy and ultrasound, 41 had disease fully assessed on ultrasound only, with complications in 26/41. Twenty-nine of 41 had mild or no endoscopic inflammation with moderate or severe disease on ultrasound at the same segment or at a segment proximal to the reach of the endoscope. Changes in management were significantly attributed to ultrasound in 22 of these 29 patients. Conclusion. The benefit of cross-sectional imaging is invaluable for the comprehensive assessment of bowel not shown on ileocolonoscopy. Ultrasound may make a significant contribution to correct classification of disease extent and severity of Crohn's disease. Prospective studies are needed to further understand the contribution of US in patient management.
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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 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".