Predicting Outcomes to Optimize Disease Management in Inflammatory Bowel Diseases
Bibliographic record
Abstract
BACKGROUND AND AIMS: Efforts to slow or prevent the progressive course of inflammatory bowel diseases [IBD] include early and intensive monitoring and treatment of patients at higher risk for complications. It is therefore essential to identify high-risk patients - both at diagnosis and throughout disease course. METHODS: As a part of an IBD Ahead initiative, we conducted a comprehensive literature review to identify predictors of long-term IBD prognosis and generate draft expert summary statements. Statements were refined at national meetings of IBD experts in 32 countries and were finalized at an international meeting in November 2014. RESULTS: Patients with Crohn's disease presenting at a young age or with extensive anatomical involvement, deep ulcerations, ileal/ileocolonic involvement, perianal and/or severe rectal disease or penetrating/stenosing behaviour should be regarded as high risk for complications. Patients with ulcerative colitis presenting at young age, with extensive colitis and frequent flare-ups needing steroids or hospitalization present increased risk for colectomy or future hospitalization. Smoking status, concurrent primary sclerosing cholangitis and concurrent infections may impact the course of disease. Current genetic and serological markers lack accuracy for clinical use. CONCLUSIONS: Simple demographic and clinical features can guide the clinician in identifying patients at higher risk for disease complications at diagnosis and throughout disease course. However, many of these risk factors have been identified retrospectively and lack validation. Appropriately powered prospective studies are required to inform algorithms that can truly predict the risk for disease progression in the individual patient.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".