A global consensus on the classification, diagnosis and multidisciplinary treatment of perianal fistulising Crohn's disease
Bibliographic record
Abstract
OBJECTIVE: To develop a consensus on the classification, diagnosis and multidisciplinary treatment of perianal fistulising Crohn's disease (pCD), based on best available evidence. METHODS: Based on a systematic literature review, statements were formed, discussed and approved in multiple rounds by the 20 working group participants. Consensus was defined as at least 80% agreement among voters. Evidence was assessed using the modified GRADE (Grading of Recommendations Assessment, Development, and Evaluation) criteria. RESULTS: Highest diagnostic accuracy can only be established if a combination of modalities is used. Drainage of sepsis is always first line therapy before initiating immunosuppressive treatment. Mucosal healing is the goal in the presence of proctitis. Whereas antibiotics and thiopurines have a role as adjunctive treatments in pCD, anti-tumour necrosis factor (anti-TNF) is the current gold standard. The efficacy of infliximab is best documented although adalimumab and certolizumab pegol are moderately effective. Oral tacrolimus could be used in patients failing anti-TNF therapy. Definite surgical repair is only of consideration in the absence of luminal inflammation. CONCLUSIONS: Based on a multidisciplinary approach, items relevant for fistula management were identified and algorithms on diagnosis and treatment of pCD were developed.
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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 | 0.087 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.016 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".