The risks of post-operative complications following pre-operative infliximab therapy for Crohn's disease in patients undergoing abdominal surgery: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Infliximab is an anti-TNF alpha blocker frequently utilized in the management of moderate to severe Crohn's Disease. The immunosuppressive effects of infliximab may increase the risk for post-operative complications among Crohn's Disease patients undergoing abdominal surgery. We conducted a systematic review and meta-analysis of studies comparing the rates of post-operative complications among Crohn's disease patients treated with Infliximab therapy versus alternative therapies. METHODS: We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and searched 4 electronic databases along with major conference abstract databases from inception of database until November, 2012. English-language articles and abstracts evaluating post-operative complications among Crohn's disease patients were considered eligible. We applied meta-analysis with random effects model to calculate the overall odds ratio for total major complications as well as several secondary outcomes. RESULTS: Data were extracted from six studies including 1159 patients among whom 413 complications were identified. The most common complications were wound infections, anastomotic leak and sepsis. There was no significant difference in the major complication rate (OR=1.59[95% CI: 0.89-2.86]; p=0.15), minor complication rate (OR=1.80 [CI: 0.87-3.71]; p=0.11), reoperation rate (OR=1.33 [CI: 0.55-3.20]; p=0.52) or 30 day mortality rate (OR=3.74 [CI: 0.56-25.16]; p=0.13) between the Infliximab and control groups. CONCLUSIONS: This meta analysis provides some evidence that infliximab may be safe to continue in the pre-operative period without increasing the risk of post-operative complications for Crohn's disease patients undergoing abdominal surgery.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.052 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".