Leaving behind the Mucosa: Advances and Future Directions of Intestinal Ultrasound in Ulcerative Colitis
Bibliographic record
Abstract
Inflammatory Bowel Diseases (IBD), mainly Ulcerative Colitis (UC) and Crohn's Disease (CD), are disorders characterized by chronic inflammation with severe morbidity and long-term disabling quality of life outcomes. UC mainly affects the mucosal and sub-mucosal layers of the colon, without embracing the peri-intestinal structures. Considering the predominant mucosal location of UC inflammation, the implementation of transmural evaluation by cross-sectional imaging techniques, mainly Intestinal Ultrasound (IUS), has been left behind for ages, especially if compared to CD. Nevertheless, studies analyzing intestinal ultrasound parameters accuracy in disease activity detection reported a good-to-optimal correlation of IUS markers with colonic inflammation, suggesting comparable feasibility of IUS monitoring in UC as in CD. The easy-to-use, costless and point-of-care available status of IUS is therefore crucial in order to improve the diagnostic process and, according to the recent literature, to monitor the response to treatment leading to speeding up decision making and therapy adjustments. Recent studies have demonstrated the correlation between transmural healing in UC with favorable outcomes even in the long term. An evidence gap still exists in the assessment of the rectum, with trans-perineal ultrasound (TPUS) a potential answer to reach a more precise evaluation of rectal inflammation. Eventually, IUS is also increasingly showing promises in emergent or post-surgical UC settings, considering various efforts put in line to demonstrate its feasibility in predicting response to salvage therapy for surgery avoidance and in studying inflammation relapse after procto-colectomy with ileo-pouch-anal anastomosis (IPAA) creation.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".