Defining Disease Severity in Inflammatory Bowel Diseases: Current and Future Directions
Bibliographic record
Abstract
Although most treatment algorithms in inflammatory bowel disease (IBD) begin with classifying patients according to disease severity, no formal validated or consensus definitions of mild, moderate, or severe IBD currently exist. There are 3 main domains relevant to the evaluation of disease severity in IBD: impact of the disease on the patient, disease burden, and disease course. These measures are not mutually exclusive and the correlations and interactions between them are not necessarily proportionate. A comprehensive literature search was performed regarding current definitions of disease severity in both Crohn's disease and ulcerative colitis, and the ability to categorize disease severity in a particular patient. Although numerous assessment tools for symptoms, quality of life, patient-reported outcomes, fatigue, endoscopy, cross-sectional imaging, and histology (in ulcerative colitis) were identified, few have validated thresholds for categorizing disease activity or severity. Moving forward, we propose a preliminary set of criteria that could be used to classify IBD disease severity. These are grouped by the 3 domains of disease severity: impact of the disease on the patient (clinical symptoms, quality of life, fatigue, and disability); measurable inflammatory burden (C-reactive protein, mucosal lesions, upper gastrointestinal involvement, and disease extent), and disease course (including structural damage, history/extension of intestinal resection, perianal disease, number of flares, and extraintestinal manifestations). We further suggest that a disease severity classification should be developed and validated by an international group to develop a pragmatic means of identifying patients with severe disease. This is increasingly important to guide current therapeutic strategies for IBD and to develop treatment algorithms for clinical practice.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".