Gastrointestinal ultrasound in inflammatory bowel disease: an underused resource with potential paradigm-changing application
Bibliographic record
Abstract
Evolution of treatment targets in IBD has increased the need for objective monitoring of disease activity to guide therapeutic strategy. Although mucosal healing is the current target of therapy in IBD, endoscopy is invasive, expensive and unappealing to patients. GI ultrasound (GIUS) represents a non-invasive modality to assess disease activity in IBD. It is accurate, cost-effective and reproducible. GIUS can be performed at the point of care without specific patient preparation so as to facilitate clinical decision-making. As compared with ileocolonoscopy and other imaging modalities (CT and MRI), GIUS is accurate in diagnosing IBD, detecting complications of disease including fistulae, strictures and abscesses, monitoring disease activity and detecting postoperative disease recurrence. International groups increasingly recognise GIUS as a valuable tool with paradigm-changing application in the management of IBD; however, uptake outside parts of continental Europe has been slow and GIUS is underused in many countries. The aim of this review is to present a pragmatic guide to the positioning of GIUS in IBD clinical practice, providing evidence for use, algorithms for integration into practice, training pathways and a strategic implementation framework.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".