Review article: Measuring disease severity in inflammatory bowel disease – Beyond treat to target
Bibliographic record
Abstract
BACKGROUND: Inflammatory bowel disease (IBD) follows a heterogenous disease course and predicting a patient's prognosis is challenging. There is a wide burden of illness in IBD and existing tools measure disease activity at a snapshot in time. Comprehensive assessment of IBD severity should incorporate disease activity, prognosis, and the impacts of disease on a patient. This review investigates the concept of disease severity in adults with IBD to highlight key components contributing to this. METHODS: To perform this narrative review, a Medline search was conducted for full-text articles available at 1st March 2024 using search terms which encompassed disease activity assessment, disease severity, prognosis, natural history of Crohn's disease (CD) and ulcerative colitis (UC), and the burden of IBD. RESULTS: Current methods of disease assessment in IBD have evolved from a focus on the burden of symptoms to one that includes inflammatory targets, genetic, serological, and proteomic profiles, and assessments of quality-of-life (QoL), disability, and psychosocial health. Longitudinal studies of IBD suggest that the burden of illness is driven by disease phenotype, clinical markers of complicated disease course (previous intestinal resection, corticosteroid use, perianal disease in CD, recent hospitalisations in UC), gut inflammation, and the impact of IBD on the patient. CONCLUSIONS: Disease severity in IBD can be difficult to conceptualise due to the multitude of factors that contribute to IBD outcomes. Measurement of IBD severity may better encapsulate the full burden of illness rather than gut inflammation alone at a single timepoint and may be associated with longitudinal outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| 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) | 0.006 | 0.002 |
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".