Assessment of activity and severity of inflammatory bowel disease in cross-sectional imaging techniques: a systematic review
Bibliographic record
Abstract
BACKGROUND AND AIMS: Cross-sectional imaging techniques, including intestinal ultrasonography (IUS), computed tomography enterography (CTE), magnetic resonance enterography (MRE), are increasingly used for the evaluation of inflammatory bowel diseases (IBD). We aimed to systematically review literature evidence on the assessment of disease activity, and/or severity through cross-sectional imaging in IBD patients, and to offer guidance on their most effective utilization. METHODS: We performed a systematic review of PubMed, EMBASE, and Scopus to identify citations pertaining to the assessment of disease activity and/or severity at cross-sectional imaging techniques compared to a reference standard (ie, other radiological techniques, endoscopy, histopathology, and surgery) in IBD patients published until December 2023. RESULTS: Overall, 179 papers published between 1990 and 2023 were included, with a total of 10 988 IBD patients (9304 Crohn's disease [84.7%], 1206 ulcerative colitis [11.0%], 38 IBD-U [0.3%], 440 unspecified [4.0%]). Of the 179 studies, 39 investigated IUS, 22/179 CTE, and 101/179 MRE. In the remaining papers, 2 techniques were addressed together. In 81.6% of the papers, endoscopy (with or without histopathology) was used as a reference standard. All studies included evaluated disease activity, while just over half (100/179, 55.8%) also evaluated disease severity of the addressed cross-sectional methodology. Pooled sensitivity, specificity, and overall accuracy of IUS, MRE, and CTE compared to the reference standard were 60%-99%, 60%-100%, and 70%-99%, respectively. CONCLUSIONS: All cross-sectional imaging techniques demonstrated moderate-to-good accuracy in assessing disease activity and severity of IBD. This finding highlights the potential, especially for MRE and IUS to be widely utilized in managing IBD in both clinical practice and clinical trials.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".