Biomarkers That Predict Crohn’s Disease Outcomes
Bibliographic record
Abstract
Crohn's disease (CD), a chronic inflammatory condition of the digestive tract, poses significant challenges in terms of disease prognosis and treatment selection. Biomarkers have the potential to predict CD outcomes and guide clinical decision-making. This review aims to summarize the current literature on promising biomarkers associated with CD outcomes and their potential clinical implications. The identification of reliable biomarkers for CD outcomes is of paramount importance in tailoring treatment strategies, monitoring disease activity, and predicting the risk of complications. Clinical prognostic factors traditionally used to assess disease severity, and the likelihood of complications have limitations in accuracy and predictive value. Thus, there is a need for more precise biomarkers, particularly in newly diagnosed and treatment-naive patients. Pharmacogenomic markers, such as TPMT and NUDT15 polymorphisms, have been utilized to identify patients at risk of adverse events with thiopurine therapy. Several biomarkers, including HLA haplotypes, oncostatin M expression, and transcriptomic profiles, have shown associations with response to anti-TNF therapy. Confocal laser endomicroscopy and single-cell analyses hold promise in predicting treatment response to specific therapies. The identification of biomarkers associated with post-operative recurrence in CD is crucial, as it could lead to changes in management algorithms. Several promising microbiome signatures and proteomic profiles have been identified. In conclusion, biomarkers have the potential to revolutionize the management of CD by providing valuable prognostic information and guiding treatment decisions. However, further research and validation are necessary to establish their clinical utility and integration into routine 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 | 0.001 | 0.006 |
| 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 | 0.000 | 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".