Does Combined Medical and Surgical Treatment Improve Perianal Fistula Outcomes in Patients With Crohn’s Disease? A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The optimal treatment of perianal fistulizing Crohn's disease [PFCD] is unknown. We performed a systematic review with meta-analysis to compare combined surgical intervention and anti-tumour necrosis factor [anti-TNF] therapy [combined therapy] vs either therapy alone. METHODS: MEDLINE, EMBASE, and Cochrane databases were searched systematically up to end December 2023. Surgical intervention was defined as an exam under anaesthesia ± setons. We calculated weighted risk ratios [RRs] with 95% confidence intervals [CIs] for our co-primary outcomes: fistula response and healing, defined clinically as a reduction in fistula drainage or number of draining fistulas and fistula closure respectively. RESULTS: Thirteen studies were analysed: 515 patients treated with combined therapy, 330 patients with surgical intervention, and 406 patients with anti-TNF therapy with follow-up between 10 weeks and 3 years. Fistula response [RR 1.10; 95% CI 0.93-1.30, p = 0.28] and healing [RR 1.06; 95% CI 0.86-1.31, p = 0.58] was not significantly different when comparing combined therapy with anti-TNF therapy alone. In contrast, combined therapy was associated with significantly higher rates of fistula response [RR 1.25; 95% CI 1.10-1.41, p < 0.001] and healing [RR 1.17; 95% CI 1.00-1.36, p = 0.05] compared with surgical intervention alone. Our results remained stable when limiting to studies that assessed outcomes within 1 year and studies where <10% of patients underwent fistula closure procedures. CONCLUSION: Combined surgery and anti-TNF therapy was not associated with improved PFCD outcomes compared with anti-TNF therapy alone. Due to an inability to control for confounding and small study sizes, future, controlled trials are warranted to confirm these findings.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.037 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.003 | 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".