How close are we to a success stratification tool for improving biological therapy in ulcerative colitis?
Bibliographic record
Abstract
INTRODUCTION: Biological therapies have become the standard treatment for ulcerative colitis (UC). However, clinical remission rates post-induction therapy remain modest at 40-50%, with many initial responders losing response over time. Current treatment strategies frequently rely on a 'trial and error' approach, leading to prolonged periods of ineffective and costly therapies for patients, accompanied by associated treatment complications. AREA COVERED: This review discusses current evidence on risk stratification tools for predicting therapeutic efficacy and minimizing adverse events in UC management. Recent studies have identified predictive factors for biologic therapy response. In the context of personalized medicine, the goal is to identify patients at high risk of progression and complications, as well as those likely to respond to specific therapies. Essential risk stratification tools include clinical decision-making aids, biomarkers, genomics, multi-omics factors, endoscopic, imaging, and histological assessments. EXPERT OPINION: Employing risk stratification tools to predict therapeutic response and prevent treatment-related complications is essential for precision medicine in the biological management of UC. These tools are necessary to select the most suitable treatment for each individual patient, thereby enhancing efficacy and safety.
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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 | 0.018 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".