Clinical Predictors of Early and Late Endoscopic Recurrence Following Ileocolonic Resection in Crohn’s Disease
Bibliographic record
Abstract
BACKGROUND AND AIMS: Multiple factors are suggested to place Crohn's disease patients at risk of recurrence after ileocolic resection with conflicting associations. We aimed to identify clinical predictors of recurrence at first [early] and further [late] postoperative colonoscopy. METHODS: Crohn's disease patients undergoing ileocolic resection were prospectively recruited at six North American centres. Clinical data were collected and endoscopic recurrence was defined as Rutgeerts score ≥i2. A multivariable model was fitted to analyse variables independently associated with recurrence. RESULTS: A total of 365 patients undergoing 674 postoperative colonoscopies were included with a median age of 32 years, 189 [51.8%] were male, and 37 [10.1%] were non-Whites. Postoperatively, 133 [36.4%] used anti-tumour necrosis factor [anti-TNF] and 30 [8.2%] were smokers. At first colonoscopy, 109 [29.9%] had recurrence. Male gender (odds ratio [OR] = 1.95, 95% confidence interval [CI] 1.12-3.40), non-White ethnicity [OR = 2.48, 95% CI 1.09-5.63], longer interval between surgery and colonoscopy [OR = 1.09, 95% CI 1.002-1.18], and postoperative smoking [OR = 2.78, 95% CI 1.16-6.67] were associated with recurrence, while prophylactic anti-TNF reduced the risk [OR = 0.28, 95% CI 0.14-0.55]. Postoperative anti-TNF prophylaxis had a protective effect on anti-TNF experienced patients but not on anti-TNF naïve patients. Among patients without recurrence at first colonoscopy, Rutgeerts score i1 was associated with subsequent recurrence [OR = 4.43, 95% CI 1.73-11.35]. CONCLUSIONS: We identified independent clinical predictors of early and late Crohn's disease postoperative endoscopic recurrence. Clinical factors traditionally used for risk stratification failed to predict recurrence and need to be revised.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".