Disease Outcomes After Segmental Resection of Colonic Crohn’s Disease: A Retrospective Multicenter Study
Bibliographic record
Abstract
BACKGROUND: Colonic surgery for Crohn's disease (CD) frequently involves sparing uninvolved segments of the colon. Few studies have assessed recurrence rates after segmental colectomy (SC). The aim of this study was to determine the rate of and identify the risk factors for postoperative CD recurrence. METHODS: This was a multicenter retrospective study from 3 tertiary inflammatory bowel disease (IBD) referral centers of CD patients who underwent SC between 2000 and 2019. We defined endoscopic recurrence as the presence of ulcers in the remaining colon upon postoperative colonoscopy. RESULTS: A total of 108 patients were included. Sixty-nine (63.9%) patients had evidence of postoperative CD endoscopic recurrence. Age at surgery <40 years and disease duration ≤156 months predicted an increased likelihood for postoperative recurrence (odds ratio [OR], 2.43; P = .031 and OR, 3.29; P = .005, respectively), whereas abdominal perineal resection (OR, 0.21; P = .005), indication for SC of malignancy (OR, 0.14; P = .016), and postoperative use of tumor necrosis factor α (TNFα) inhibitor for prophylactic purposes (OR, 0.38; P = .040) negatively predicted disease recurrence. Disease duration ≤156 months (OR, 2.86; P = .039) and postoperative TNFα inhibitor prophylaxis remained significant (OR, 0.26; P = .013) upon multivariable modeling. CONCLUSION: Although high rates of recurrence persist within the postoperative phase of SC for CD, the postoperative use of TNFα inhibitor for prophylactic purposes for a subset of patients may promote a more durable endoscopic remission.
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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 | 0.002 |
| 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 | 0.001 |
| 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".