Biologic Therapies for the Treatment of Post-ileal Pouch Anal Anastomosis Surgery Chronic Inflammatory Disorders: Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: Chronic inflammatory disorders after ileal pouch-anal anastomosis (IPAA) surgery are common. These include chronic pouchitis (CP), Crohn's disease (CD) of the pouch, prepouch ileitis (PI) and rectal cuff inflammation (cuffitis). The aim of this study was to evaluate the efficacy of biologic therapies in treating these disorders. Method: Systematic review of all published studies from inception to August 1, 2021 was performed to investigate the efficacy of biologic therapies for post-IPAA chronic inflammatory disorders. The primary outcome was the efficacy of biologic therapies in achieving complete clinical response in patients with IPAA. Results: A total of 26 studies were identified including 741 patients. Using a random-effect model, the efficacy of infliximab in achieving complete clinical response in patients with CP was 51% (95% CI, 36 to 66), whereas the efficacy of adalimumab was 47% (95% CI, 31 to 64). The efficacies of ustekinumab and vedolizumab were 41% (95% CI, 06 to 88) and 63% (95% CI, 35 to 84), respectively. In patients with CD/PI, the efficacy of infliximab in achieving complete clinical response was 52% (95% CI, 33 to 71), whereas the efficacy of adalimumab was 51% (95% CI, 40 to 61). The efficacies of ustekinumab and vedolizumab were 42% (95% CI, 06 to 90) and 67% (95% CI, 38 to 87), respectively. Only one study involved patients with cuffitis. Conclusion: Ustekinumab, infliximab, vedolizumab and adalimumab are effective in achieving complete clinical response in post-IPAA surgery chronic inflammatory disorders. More studies are needed to determine the efficacy of biologics in cuffitis.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.025 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".