Future directions in inflammatory bowel disease management
Bibliographic record
Abstract
BACKGROUND AND AIMS: Clinical management of inflammatory bowel diseases (IBD), new treatment modalities and the potential impact of personalised medicine remain topics of intense interest as our understanding of the pathophysiology of IBD expands. METHODS: Potential future strategies for IBD management are discussed, based on recent preclinical and clinical research. RESULTS: A top-down approach to medical therapy is increasingly being adopted for patients with risk factors for severe inflammation or an unfavourable disease course in an attempt to halt the inflammatory process as early as possible, prevent complications and induce mucosal healing. In the future, biological therapies for IBD are likely to be used more selectively based on personalised benefit/risk assessment, determined through reliable biomarkers and tissue signatures, and will probably be optimised throughout the course of treatment. Biologics with different mechanisms of action will be available; when one drug fails, patients will be able to switch to another and even combination biologics may become a reality. The role of biotherapeutic products that are similar to currently licensed biologics in terms of quality, safety and efficacy - i.e. biosimilars - is at an early stage and requires further experience. Other therapeutic strategies may involve manipulation of the microbiome using antibiotics, probiotics, prebiotics, diet and combinations of all these approaches. Faecal microbiota transplantation is also a potential option in IBD although controlled data are lacking. CONCLUSIONS: The future of classifying, prognosticating and managing IBD involves an outcomes-based approach to identify biomarkers reflecting various biological processes that can be matched with clinically important endpoints.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".