Noninvasive Fecal Immunochemical Testing and Fecal Calprotectin Predict Mucosal Healing in Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND: The noninvasive biomarkers fecal immunochemical testing (FIT) and fecal calprotectin (FCP) are sensitive for prediction of mucosal inflammation in inflammatory bowel disease. However, neither test has yet been shown to independently and accurately predict mucosal healing (MH). We aimed to assess the specificity of noninvasive FIT and FCP for MH prediction. METHODS: In this prospective cohort study of adult inflammatory bowel disease outpatients presenting for colonoscopy, stool samples for FIT and FCP were collected 48 hours before endoscopy. Using MH defined by Simple Endoscopic Score for Crohn's disease (SES-CD = 0), Rutgeert's score (i0), and the Ulcerative Colitis Endoscopic Index of Severity (UCEIS = 3), receiver operator characteristic curves were plotted, and sensitivity, specificity, positive and negative predictive values, and areas under the curve were calculated. Multivariate logistic regression analysis was used to develop a clinical model for noninvasively predicting MH. RESULTS: Eighty patients (40 Crohn's disease and 40 ulcerative colitis) were enrolled. The specificities of FIT <100 ng/mL and FCP <250 μg/g for MH were 0.57 (95% confidence interval, 0.38-0.74) and 0.77 (0.57-0.89), respectively. Positive predictive values for MH for FIT <100 ng/mL and FCP <250 μg/g were 0.78 (0.64-0.87) and 0.77 (0.58-0.90), respectively. In multivariate modeling, combining FIT, FCP, and clinical symptomatic remission improved specificity for MH to 0.90 (0.72-0.97) with positive predictive values of 0.84 (0.60-0.96). Areas under the curve for FIT was higher for patients with ulcerative colitis (0.88) than for patients with Crohn's disease (0.69, P = 0.05). CONCLUSIONS: FIT and FCP have similar performance characteristics for identifying MH. Combined, low FIT, low FCP, and clinical remission are specific for MH.
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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 | 0.006 |
| 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 | 0.000 | 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".