Perianal Fistulizing Crohn’s Disease: Utilizing the TOpClass Classification in Clinical Practice to Provide Targeted Individualized Care
Bibliographic record
Abstract
BACKGROUND & AIMS: Perianal fistulation is a challenging phenotype of Crohn's disease, with significant impact on quality of life. Historically, fistulae have been classified anatomically in relation to the sphincter complex, and management guidelines have been generalized, with lack of attention to the clinical heterogenicity seen. The recent 'TOpClass classification system' for perianal fistulizing Crohn's disease (PFCD) addresses this issue, and classifies patients into defined groups, which provide a focus for fistula management that aligns with disease characteristics and patient goals. In this article, we discuss the clinical applicability of the TOpClass model and provide direction on its use in clinical practice. METHODS: An international group of perianal clinicians participated in an expert consensus to define how the TOpClass system can be incorporated into real-life practice. This included gastroenterologists, inflammatory bowel disease surgeons, and radiologists specialized in PFCD. The process was informed by the multi-disciplinary team management of 8 high-volume fistula centres in North America, Europe, and Australia. RESULTS: The process produced position statements to accompany the classification system and guide PFCD management. The statements range from the management of patients with quiescent perianal disease to those with severe PFCD requiring diverting-ostomy and/or proctectomy. The optimization of medical therapies, as well as the use of surgery, in fistula closure and symptom management is explored across each classification group. CONCLUSION: This article provides an overview of the system's use in clinical practice. It aims to enable clinicians to have a pragmatic and patient goal-centered approach to medical and surgical management options for individual patients with PFCD.
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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 | 0.022 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".