Impact of Small Bowel MRI in Routine Clinical Practice on Staging of Crohn's Disease
Bibliographic record
Abstract
BACKGROUND AND AIMS: Small bowel visualisation is a complex diagnostic approach, but mandatory for risk stratification and stage-adjusted therapy in Crohn's disease. Current guidelines favour transabdominal ultrasound and small bowel MRI as methods of choice, although their clinical impact in daily practice remains controversial. The aim of this study was to evaluate the diagnostic benefit of small bowel MRI in Crohn's disease according to Montreal Classification, in routine practice. METHODS: Patients who underwent MR-enterography [MRE] or MR-enteroclysis [MRY] were included in a retrospective single-centre study. MRI findings were correlated with results from clinical work-up and evaluated in terms of [1] diagnostic yield, [2] significant additional information, and [3] alterations in the assessment of disease behaviour and location according to Montreal Classification. RESULTS: A total of 347 small bowel MRI examinations were analysed [MRE: 49 / MRY: 298]. MRI had an average sensitivity/specificity of 82.5% and 99.9% [positive predictive value: 99.8% / negative predictive value: 91.1%] respectively. In every second patient, new relevant diagnostic information was provided. Incorporation of the MRI results caused significant shifts in Montreal Classification, specifically higher L-levels [+21.2%; p < 0.05] and higher B-levels: [+24.6%; p < 0.05]. CONCLUSIONS: Even in routine practice, small bowel MRI is a powerful and reliable technique in small bowel work-up. Since MRE and MRY presented high diagnostic yields, often detected significant additional information, and significantly caused shifts in Montreal Classification, both techniques are confirmed to be excellent tools in diagnosing and monitoring Crohn's disease in its daily course.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".