Risk Factors for Repetitive Ileocolic Resection in Patients with Crohnʼs Disease
Bibliographic record
Abstract
BACKGROUND: Surgical recurrence rates among patients with Crohn's disease with ileocolic resection (ICR) remain high, and factors predicting surgical recurrence remain controversial. We aimed to identify risk and protective factors for repetitive ICRs among patients with Crohn's disease in a large cohort of patients. METHODS: Data on 305 patients after first ICR were retrieved from our cross-sectional and prospective database (median follow-up: 15 yr [0-52 yr]). Data were compared between patients with 1 (ICR = 1, n = 225) or more than 1 (ICR >1, n = 80) resection. Clinical phenotypes were classified according to the Montreal Classification. Gender, family history of inflammatory bowel disease, smoking status, type of surgery, immunomodulator, and biological therapy before, parallel to and after first ICR were analyzed. RESULTS: The mean duration from diagnosis until first ICR did not differ significantly between the groups, being 5.93 ± 7.65 years in the ICR = 1 group and 5.36 ± 6.35 years in the ICR >1 group (P = 0.05). Mean time to second ICR was 6.7 ± 5.74 years. In the multivariate logistic regression analysis, ileal disease location (odds ratio [OR], 2.42; 95% confidence interval [CI], 1.02-5.78; P = 0.05) was a significant risk factor. A therapy with immunomodulators at time of or within 1 year after first ICR (OR, 0.23; 95% CI, 0.09-0.63; P < 0.01) was a protective factor. Neither smoking (OR, 1.16; 95% CI, 0.66-2.06) nor gender (male OR, 0.85; 95% CI, 0.51-1.42) or family history (OR, 1.68; 95% CI, 0.84-3.36) had a significant impact on surgical recurrence. CONCLUSIONS: Immunomodulators have a protective impact regarding surgical recurrence after ICR. In contrast, ileal disease location constitutes a significant risk factor for a second ICR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".