Small bowel imaging in Crohn’s disease with a special focus on obesity, pregnancy and postsurgical assessment
Bibliographic record
Abstract
Crohn’s disease (CD) is an immune-mediated, multisystem inflammatory disorder characterised by discontinuous transmural, sometimes granulomatous, inflammation of the gastrointestinal tract. Although it can occur anywhere in the gastrointestinal tract, it has a 70% predilection for the terminal ileum. Ileocolonoscopy with biopsy remains the gold standard for initial diagnosis and assessment of CD activity but has several limitations, including invasiveness, risk of complications and cost. With a shifting focus towards treatment targets including transmural healing, non-invasive imaging modalities are being used increasingly to assess the small bowel, particularly the terminal ileum. CT enterography, magnetic resonance enterography and gastrointestinal ultrasound are widely used for small bowel imaging in clinical practice and have relatively good sensitivity and specificity. Obesity is a growing problem for patients with CD and is associated with limitations in medical imaging. Equally, cross-sectional imaging in pregnant and postsurgical patients with CD has its own challenges. In this article, we review small bowel imaging in CD with a special focus on obesity, pregnancy and postsurgical assessment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".