Pharmacological Therapies for the Management of Fistulizing Crohn’s Disease: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Fistulas are a debilitating complication of Crohn's disease [CD]. We conducted a systematic review to assess the efficacy of medical therapies for fistulizing CD. METHODS: MEDLINE, Embase, and CENTRAL were searched on May 26, 2022, for randomized controlled trials [RCTs] of pharmacological therapy in adults with fistulizing CD. The primary outcome was induction and maintenance of fistula response. Pooled risk ratios [RRs] and 95% confidence intervals [CIs] were calculated. GRADE was used to assess the certainty of evidence. RESULTS: Thirty-eight RCTs were included. Nineteen trials [50%] exclusively involved perianal fistula. The remaining studies included some participants with non-perianal fistula. Pooled RRs for anti-tumour necrosis factor [TNF] agents were not statistically significant for induction [RR 1.36, 95% CI 0.97-1.91] or maintenance of fistula response [RR 1.48, 95% CI 0.97-2.27]. However, in a sensitivity analysis of studies with fistula response as the primary outcome, anti-TNFs were superior to placebo for induction [RR 1.94, 95% CI 1.10-3.41] and maintenance [RR 1.88, 95% CI 1.23-2.88] of fistula response. Oral small molecules [RR 2.56, 95% CI 1.18-5.53] and mesenchymal stem cell [MSC] therapy [RR 1.26, 95% CI 1.01-1.57] were effective for induction of fistula response. Ustekinumab was associated with maintenance of fistula response [RR 1.80, 95% CI 1.04-3.11]. Vedolizumab was not superior to placebo. The certainty of evidence ranged from very low to moderate. CONCLUSION: Very low- to moderate-certainty evidence suggests that anti-TNF therapy, oral small molecules, ustekinumab, and MSCs are effective for perianal fistulizing CD. Dedicated fistula studies evaluating biologics and small molecules are needed.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".