Management and treatment optimization of patients with mild to moderate ulcerative colitis
Bibliographic record
Abstract
INTRODUCTION: Ulcerative colitis (UC) is a chronic inflammatory bowel disease with a significant health-care burden worldwide. While medical therapy aims to induce and maintain remission, optimal management of mild to moderate UC remains challenging due to heterogeneity in severity classifications and non-standardized approaches. This comprehensive review summarizes current evidence and knowledge gaps to optimize clinical decision-making in patients with mild to moderate UC. AREAS COVERED: After an extensive literature search of PubMed, Medline, and Embase through August 2023, we provide an overview of definitions utilized to characterize mild to moderate UC severity and established therapeutic targets. Current medical treatments including mesalazine formulations, corticosteroids, and their combinations are surveyed. The role of emerging intestinal ultrasound, telemedicine, and home testing is explored. Individualized, patient-centered paradigms aiming to streamline care delivery through proactive identification of relapses are also examined. EXPERT OPINION: Addressing inconsistencies in disease activity stratification will better align tailored regimens with each patient's profile. Advancing noninvasive technologies like ultrasound criteria and home testing could improve UC management by enabling personalized models. Realizing individualized plans through informed shared-decision making between health-care providers and fully engaged patients holds promise to maximize quality of life outcomes. Continuous improvement relies on innovation bridging different domains to overcome current limitations and push the field toward more predictive and tailored care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".