Enterography characteristics can determine the need for surgery in patients with stricturing Crohnʼs disease
Bibliographic record
Abstract
Surgery remains the primary modality for treating Crohn's disease (CD) strictures. Literature on stricture characteristics that predict aggressive disease course is very limited. We hypothesized that CT enterography (CTE)- and MR enterography (MRE)- based characteristics can predict the need for surgery in stricturing CD. A historical cohort study was performed on CD patients who underwent CTE or MRE in 2009 in our IBD Center and were confirmed to have strictures. Imaging characteristics such as stricture length, number, location, proximal bowel dilation, the presence of fistula, inflammatory mass or abscess, mucosal hyperenhancement and bowel wall thickening were abstracted from radiology reports. Kaplan-Meier and Cox Proportional Hazards models were used to predict the need for surgery. In our study cohort of 164 patients (mean age 38.4 ± 15 years, 56.7 % females), 87 (54%) had surgery with a median follow-up of 607 days. Univariate analysis showed that patients with earlier disease onset (Montreal A1 or A2), fistulizing disease, mesenteric stranding, proximal bowel dilation >3 centimeter, mass or abscess were significantly more likely to have surgery. In contrast, length and number of strictures, bowel wall thickening or mucosal hyperenhancement were not significant predictors of surgery. An enterography-based cumulative risk-score derived from multivariate analysis was successfully able to differentiate strictures at different risk for surgery (p<0.0001) (see Figure). Surgery-free survival for patients stratified by enterography-based cumulative risk score (p<0.0001). Scores “0” and “1” represent low-risk strictures and scores >= “2” represent high-risk strictures Certain CTE or MRE characteristics can predict the need for surgery in patients with stricturing CD. These characteristics can be used to develop a novel stricture severity score (SSS) that can allow prognostication and optimal decision making in patients with structuring CD at the point-of-care.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".