Assessment of Disease Behavior in Patients with Crohn’s Disease by MR Enterography
Bibliographic record
Abstract
BACKGROUND: Magnetic resonance imaging (MRI) of the bowel is an increasingly used modality to evaluate patients with Crohn's disease. The Montreal classification of the disease behavior is considered as an excellent prognostic and therapeutic parameter for these patients. In our study, we correlated the behavior assessment performed by a radiologist based on MRI with the surgeons' clinical assessment based on the assessment during abdominal surgery. METHODS: We evaluated 76 patients with Crohn's disease, who underwent bowel resection and had an MRI within 4 weeks before surgery. Radiological behavior assessment was performed by 2 radiologists based on MRI. Behavior was classified into B1 (nonstricturing and nonpenetrating), B2, and B3 (penetrating) disease. Surgical assessment was done by the same surgeon, who performed all bowel resections, based on intraoperative findings and histologic results. RESULTS: The surgical assessment identified 4 patients (5%) as B1, 16 patients (21%) as B2, and 56 patients (74%) as B3. In 97% (n = 74) of all patients, the intraoperative and radiological assessment were identical with interobserver agreement of 0.937. In one case, B2 was radiological considered as B1, and in another case, B3 was diagnosed as B2. The diagnosis of a stricture had the highest sensitivity of 96%, whereas the detection of inflammatory mass showed the lowest sensitivity of 81%. Abscesses had the lowest positive predictive value of 68% with a specificity of 88%. Best correlation was found for fistulae (0.895). CONCLUSIONS: MRI represents an excellent imaging modality to correctly assess the Montreal classification-based disease behavior in patients scheduled for bowel resection with Crohn's disease.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".