Anti-Tumor Necrosis Factor Therapy Is Associated With Infections after Abdominal Surgery in Crohn's Disease Patients
Bibliographic record
Abstract
OBJECTIVES: Anti-tumor necrosis factor (anti-TNF) therapy effects on postoperative complications in Crohn's disease (CD) patients are unclear. We examined a retrospective cohort to clarify this relationship. METHODS: CD patients followed at a referral center between July 2004 and May 2011 who underwent abdominal surgery were identified. Postoperative complications (major infection, intra-abdominal abscess, peritonitis, anastomotic leak, wound infection, dehiscence, fistula, thrombotic, and death) were compared in patients exposed and unexposed to anti-TNF ≤8 weeks preoperatively. Demographics, surgical history, comorbidities, corticosteroid (CS) and immunomodulator use, Montreal classification, operative details, and preoperative nutritional status were assessed. Multivariate analysis measured the independent effect of preoperative anti-TNF on postoperative complications. RESULTS: Overall, 325 abdominal surgeries were performed; 150 (46%) with anti-TNF ≤8 weeks before surgery. The anti-TNF group developed overall infectious (36% vs. 25%, P=0.05) and a trend toward surgical site complications (36% vs. 25%, P=0.10) more frequently. Major postoperative and intra-abdominal septic complications did not differ between groups. Multivariable analysis showed that preoperative anti-TNF was an independent predictor of overall infectious (odds ratio (OR) 2.43; 95% confidence interval (CI) 1.18-5.03) and surgical site (OR 1.96; 95% CI 1.02-3.77) complications. CONCLUSIONS: In a tertiary referral center, use of anti-TNF therapy in CD patients ≤8 weeks before intestinal resection or any intra-abdominal surgery was independently associated with increases in infectious and surgical complications.
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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 | 0.000 | 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) | 0.002 | 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".