The Role of Biomarkers in Treatment Algorithms for Ulcerative Colitis (<scp>UC</scp>)
Bibliographic record
Abstract
This chapter reviews the available data on the serum, fecal, endoscopic, and histologic biomarkers currently available for the evaluation of ulcerative colitis (UC). Histopathology can provide important prognostic information in UC. Specifically, lower rates of corticosteroid-dependence, hospitalization, colectomy, and colorectal cancer are observed in patients with histopathologic remission than those with persistent microscopic inflammation. As a result, histopathology has been used as a biomarker for disease activity. The use of biomarkers in clinical practice has been used to identify patients whose symptoms require further investigation, to follow response to therapy, and to prognosticate disease severity. The most recent selecting therapeutic targets in inflammatory bowel disease guidelines for clinical practice targets suggest the use of an objective marker and symptoms to determine response to therapy. However, the guidelines state that fecal calprotectin should not be used as a treatment objective rather a persistently abnormal value should prompt 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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".