The Impact of Setons on Perianal Fistula Outcomes in Patients With Crohn's Disease Treated With Anti‐<scp>TNF</scp> Therapy: A Multicentre Study
Bibliographic record
Abstract
BACKGROUND AND AIMS: We aimed to assess the impact of setons on perianal fistula outcomes in patients with perianal fistulising Crohn's disease (PFCD) treated with anti-TNF therapy. METHODS: We included patients treated with their first anti-TNF therapy for PFCD after undergoing a pelvic MRI between 2005 and 2022 from 6 North American centres. Our exposure was one or more setons at the time of anti-TNF therapy. Our primary outcome was major adverse fistula outcome (MAFO), a composite of repeat local surgical intervention, hospitalisation, or faecal diversion for PFCD, and our secondary outcome was fistula remission defined clinically. We used 1:1 cardinality matching and propensity score weighting to control for fistula severity based on centrally read MRIs, luminal characteristics, and concomitant therapies. RESULTS: Our analysis included 221 patients: 81 with setons and 140 without setons. After cardinality matching, our cohorts were balanced (standardised difference < 0.1 for all covariates). Patients with setons had similar rates of MAFO (HR 1.23; 95% CI, 0.68-2.21) and fistula remission at 6 months (OR, 0.81; 95% CI, 0.41-1.59) and 12 months (OR, 0.63; 95% CI, 0.31-1.27) compared to patients without setons. Our results remained stable when analysed by propensity score weighting and in a sensitivity analysis of patients who underwent an exam under anaesthesia. In patients with abscesses, there were lower rates of MAFO (HR, 0.49; 95% CI, 0.19-1.25) but not statistically significant in patients with setons. CONCLUSIONS: In this multicentre, setons were not associated with improved fistula outcomes. Future prospective controlled studies are warranted.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".