Medical Imaging in Small Bowel Crohnʼs Disease—Computer Tomography Enterography, Magnetic Resonance Enterography, and Ultrasound
Bibliographic record
Abstract
BACKGROUND: Small bowel imaging in Crohn's disease (CD) is an important adjunct to endoscopy for the diagnosis, assessment of postoperative recurrence, and detection of complications. The best imaging modality for such indications though remains unclear. This systematic review aims to identify the imaging modality of choice considering the use of ultrasound (US), computed tomography enterography (CTE), and magnetic resonance enterography (MRE). METHODS: Databases were systematically searched for studies pertaining to the performance of US, CTE, and MRE, as compared with a predefined reference standard in the assessment of small bowel CD. RESULTS: Thirty-three studies, from a total of 1427 studies, were included in the final analysis. A comparable performance was demonstrated for MRE, CTE, and US for the diagnosis of small CD. Ultrasound was found to have the highest accuracy in the differentiation of inflammation and fibrosis. Postoperative recurrence detection was feasible with the use of MRE and US. All 3 modalities were shown to have a role in the detection of small bowel CD complications. The radiation exposure associated with CTE can be minimized by using lower radiation protocols. CONCLUSIONS: Ultrasound, CTE, and MRE all play an important role in the diagnosis and management of small bowel CD, with preference for a particular modality being influenced by specific indication, institution resources, and patient preference.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".