The potential for medical therapies to address fistulizing Crohn’s disease: a state-of-the-art review
Bibliographic record
Abstract
INTRODUCTION: Crohn's disease (CD) is a chronic, relapsing immune mediated disease, which is one of the two major types of inflammatory bowel disease (IBD). Fistulizing CD poses a significant clinical challenge for physicians. Effective management of CD requires a multidisciplinary approach, involving a gastroenterologist and a GI surgeon while tailoring treatment to each patient's unique risk factors, clinical representations, and preferences. AREAS COVERED: This comprehensive review explores the intricacies of fistulizing CD including its manifestations, types, impact on quality of life, management strategies, and novel therapies under investigation. EXPERT OPINION: Antibiotics are often used as first-line therapy to treat symptoms. Biologics that selectively target TNF-α, such infliximab (IFX), have shown high efficacy in randomized controlled trials. However, more than 50% of patients lose response to IFX, prompting them to explore alternative strategies. Current options include adalimumab and certolizumab pegol combination therapies, as well as small-molecule drugs targeting Janus kinases such as Upadacitinib. Furthermore, a promising treatment for complex fistulas is mesenchymal stem cells such as Darvadstrocel (Alofisel), an allogeneic stem cell-based therapy. However, surgical interventions are necessary for complex cases or intra-abdominal complications. Setons and LIFT procedures are the most common surgical options.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".