Biomarkers as Potential Treatment Targets in Inflammatory Bowel Disease: A Systematic Review
Bibliographic record
Abstract
There is increasing interest in the concept of 'treat-to-target' in inflammatory bowel disease as a mechanism to standardize management and prevent complications. While clinical, radiographic and endoscopic treatment end points will figure prominently in this promising management paradigm, the role that noninvasive biomarkers will play is currently undefined. The goal of the present systematic review was to investigate the potential value of biomarkers as treatment targets in inflammatory bowel disease, with particular focus on those best studied: serum C-reactive protein (CRP) and fecal calprotectin. In Crohn disease, elevated CRP levels at baseline predict response to anti-tumour necrosis factor agents, and normalization is usually associated with clinical and endoscopic remission. CRP and hemoglobin levels can be used to help predict clinical relapse in the context of withdrawal of therapy. Ultimately, the authors conclude that currently available biomarkers should not be used as treatment targets in inflammatory bowel disease because they have inadequate operational characteristics to make them safe surrogates for clinical, endoscopic and radiographic evaluation. However, CRP and fecal calprotectin are important adjunctive measures that help alert the clinician to pursue further investigation.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".