P212 Evaluation of the accuracy of MR-Enterography and CT-Enterography in the preoperative measurement of small bowel segments affected by Crohn's Disease: a single center experience
Bibliographic record
Abstract
Abstract Background The surgical treatment of Crohn's Disease (CD) must deal with heterogeneous intra-operative scenarios, multiple disease locations and the risk of short bowel syndrome related to repeated surgery. A precise preoperative assessment of the exact extension of the disease is essential to avoid extensive surgeries and to plan the best strategy. The aim of our study is to evaluate the accuracy of the pre-operative radiological assessment for predicting the intra-operative measurement of the affected bowel. Methods Consecutive patients underwent either first or repeated surgeries for ileocecal or ileal CD from November 2017 to April 2023 with an available preoperative radiological assessment trough (at most 3 months before) MR-Enterography (MR-E) or CT-Enterography (CT-E) were retrospectively included. Radiological examinations were performed according to standard protocols and each of them was independently reviewed by two experienced radiologists and the length of pathological small bowel segments were measured using a vessel analysis software. Radiological measurements were compared, using Pearson correlation coefficient, with those acquired by a single surgeon intra-operatively on the still vascularized pathologic small bowel segment. Results We included 81 patients (44% male), with a median age of 42 years [range 18-81], undergoing first (92%) or repeated surgery for fistulizing (34%) or stenosing CD. The median length of radiological measurements was 27 cm [range 3-205] while the median length of intra-operative measurements was 30 cm [range 5-165]. Pearson correlation coefficient was used to analyze the variation between the measurements and there was a statistically significant positive correlation between radiological measurements and intra-operative in-vivo small bowel lenght (r2 = 0.962; p < 0.01). Conclusion MR-E and CT-E allow precise and accurate measurements of the extent and location of intestinal tracts involved by CD. Such information is extremely important to properly plan the surgical strategy and to be able to quantify the risk of short bowel syndrome in cases of extensive disease or repeated surgery.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".