Prognostic Value of Fecal Calprotectin to Inform Treat-to-Target Monitoring in Ulcerative Colitis
Bibliographic record
Abstract
BACKGROUND & AIMS: We evaluated the value of post-induction fecal calprotectin (FCP) concentration as a biomarker in patients with ulcerative colitis (UC) treated with a biologic. METHODS: This post hoc analysis of the GEMINI 1/GEMINI LTS (N = 620) and VARSITY (N = 771) trials evaluated the cross-sectional accuracy of post-induction FCP in identifying endoscopic activity and histologic inflammation, and the prognostic performance of FCP in identifying patients most likely to achieve endoscopic and histologic remission or require colectomy and UC-related hospitalization. RESULTS: The cross-sectional accuracy of FCP in identifying endoscopic activity and histologic inflammation was modest (63%-79%). However, a post-induction FCP concentration of ≤250 μg/g vs >250 μg/g was associated with a substantially higher probability of achieving clinical remission (odds ratio [OR], 4.03; 95% confidence interval [CI], 2.78-5.85), endoscopic remission (OR, 4.26; 95% CI, 2.83-6.40), and histologic remission (Robarts Histopathology Index: OR, 5.54; 95% CI, 3.77-8.14; Geboes grade: OR, 6.42; 95% CI, 4.02-10.26) at week 52 and a lower probability of colectomy over 7 years (hazard ratio, 0.296; 95% CI, 0.130-0.677) and UC-related hospitalization (hazard ratio, 0.583; 95% CI, 0.389-0.874). The association with colectomy was significant even among patients in symptomatic remission or with endoscopic improvement post-induction, and among patients with elevated FCP at baseline. CONCLUSIONS: Although FCP had only modest cross-sectional accuracy in identifying disease activity, an FCP concentration of ≤250 μg/g vs >250 μg/g was associated with increased probability of achieving long-term clinical, endoscopic, and histologic remission, and reduced probability of colectomy and UC-related hospitalization (ClinicalTrials.gov: NCT00783718, NCT00790933, NCT02497469).
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".