Incorporating Fecal Calprotectin Into Clinical Practice for Patients With Moderate-to-Severely Active Ulcerative Colitis Treated With Biologics or Small-Molecule Inhibitors
Bibliographic record
Abstract
INTRODUCTION: We applied the Grading of Recommendations, Assessment, Development, and Evaluation framework to evaluate the performance of fecal calprotectin (FC) as an alternative to endoscopy in patients with moderate-to-severe ulcerative colitis (UC) treated with a biologic agent or tofacitinib. METHODS: Individual participant data from the trials of infliximab, golimumab, vedolizumab, and tofacitinib for UC were pooled to generate prevalence of endoscopic activity (Mayo endoscopy score) across different combinations of the rectal bleeding score (RBS) and stool frequency score (SFS). These estimates were then combined with the data from an updated systematic review of the operating properties of FC to generate clinical scenario-specific assessments of the performance of FC as a predictor of endoscopic disease activity. A prespecified threshold of acceptability for false-negative (FN) and false-positive (FP) test results was set at 5%. RESULTS: For patients with UC achieving RBS 0 + SFS 0/1, FC ≤ 50 μg/g may avoid endoscopy in 50% patients with a FN rate <5%. Similarly, for patients with RBS 2/3 + SFS 2/3, FC ≥ 250 μg/g potentially avoids endoscopy in approximately 50% patients with an FP rate <5%. The greatest uncertainty in the diagnostic performance for FC was observed in patients with UC achieving RBS 0 but having SFS 2/3, where FN and FP rates were consistently >10%, and endoscopic evaluation may be warranted. DISCUSSION: Two clinical scenarios were identified where FC can be used with confidence for monitoring treatment response to biologics or tofacitinib in patients with UC without the requirement for endoscopy.
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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 | 0.042 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".