Fecal Calprotectin Thresholds to Predict Endoscopic Remission Using Advanced Optical Enhancement Techniques and Histological Remission in IBD Patients
Bibliographic record
Abstract
BACKGROUND: Fecal calprotectin (FC) is a common surrogate marker of mucosal healing (MH) in patients with ulcerative colitis (UC) and Crohn's disease (CD). We investigated the optimum FC thresholds for defining endoscopic remission (ER) and histological remission (HR) using advanced endoscopic techniques. PATIENTS AND METHODS: In this cross-sectional study, we collected clinical, endoscopic, histological data, and FC from 76 UC and 41 CD patients. Receiver operating characteristic curves were created to evaluate the optimum cut-off of FC to predict ER evaluated by Mayo Endoscopic Score (MES), Ulcerative Colitis Endoscopic Index of Severity (UCEIS), and modified PICaSSO (Paddington International Virtual Chromoendoscopy Score) for UC patients and Simple Endoscopic Score (SES-CD) in CD patients; and HR was scored by the Robarts Histology Index (RHI) and Nancy Index for UC and modified Riley for CD. RESULTS: In UC patients, the best thresholds of FC to identify ER calculated with MES, UCEIS, and modified PICaSSO were 112, 148, and 161 mcg/g with accuracy of 86.9% 86.8%, and 81.6%, respectively. The best value of FC to predict HR was 112 mcg/g and 172 mcg/g with accuracy of 84.2% and 81.6% for RHI and Nancy Index, respectively.In CD patients, the best cut-off of FC to predict ER was 96 mcg/g with accuracy of 82.9%. The HR was best predicted by an FC value of 225 mcg/g with accuracy of 75.6%. CONCLUSIONS: The FC value threshold between 112 and 172 mcg/g could identify ER and HR in UC patients, whereas a value under 225 mcg/g should be considered for CD patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".