IBD Prediction Is Possible, but How Far Are We from Implementing It?
Bibliographic record
Abstract
Crohn's disease and ulcerative colitis, collectively known as inflammatory bowel diseases (IBDs), are chronic gastrointestinal diseases with poorly characterized pathophysiology. Recent advancements in the identification of preclinical biomarkers of IBD have shed some insight into our ability to predict or prevent these conditions. This review discusses the growing body of research on biomarkers ranging from genetics, measures of gut permeability, and microbiome signatures to circulating proteomics and metabolomics. In addition, the review will highlight the potential application of these biomarkers for early detection and risk stratification of IBD. Notably, proteomic markers such as CXCL9 and MMP-10, along with metabolic perturbations detectable prior to clinical diagnosis, provide promising avenues for understanding IBD pathogenesis and guiding prevention strategies. Furthermore, the development of integrative risk scores, combining multiomic data with demographic and lifestyle factors, could offer a personalized approach to disease prediction and prevention. While these advances present significant opportunities, challenges remain in data complexity and variability of biomarkers. This review emphasizes the importance of continued longitudinal studies and clinical trials to validate predictive models. Ultimately, the integration of early risk prediction holds the potential to reduce IBD incidence through targeted, proactive strategies.
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 | 0.030 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".