Functional validation of the TOpClass classification system for perianal fistulising Crohn’s disease using real-world multicentre data: a 10-year retrospective observational study
Bibliographic record
Abstract
Background and aims Perianal fistulising Crohn’s disease (pfCD) is an aggressive, disabling condition with significant treatment challenges. The novel TOpClass classification system aims to better capture pfCD’s disease trajectory, improving prognostication and therapeutic decision-making compared with traditional anatomical classifications. This retrospective, multicentre study aimed to provide functional validation of TOpClass and evaluate its clinical applicability in managing pfCD. Methods Data from eight expert centres included 112 patients with 10 years of follow-up. The study assessed transitions between TOpClass classifications in consecutive patients with Crohn’s disease (CD) and active perianal fistulas. Results Most patients (72%) entered the study in Class 2a or 2b, with 56% improving and 24% worsening by study end. Among those in Class 1, 58% showed disease progression, highlighting pfCD’s refractory nature. Only 23% of the population achieved fistula healing. Subanalysis of the 16 patients starting in Class 2a (eligible for fistula closure) showed significant fistula healing (n=6) in those with less complex luminal disease and shorter CD duration (1.5 vs 17 years; p=0.02) and a non-penetrating, non-stricturing (Montreal B1) phenotype (100% vs 40%; p=0.03). The study highlighted treatment challenges, with 12% of patients requiring defunctioning surgery, 77% of whom had proctectomy. Of these, 55% progressed to Class 4, emphasising the need for careful preoperative and postoperative management. Conclusion Our retrospective study demonstrates strong face validity of the TOpClass classification, supporting its clinical applicability. The findings and gaps will inform a prospective trial to confirm TOpClass’s utility and enhance our understanding of 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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".