Fecal Markers of Inflammation and Disease Activity in Pediatric Crohn Disease
Bibliographic record
Abstract
BACKGROUND: Noninvasive and accurate methods to monitor inflammatory bowel disease are required. As a planned ancillary study of the prospective ImageKids cohort, we aimed to assess the performance of fecal calprotectin (FC) with comparison to 3 fecal inflammatory markers; S100A12 (FA12), tumor pyruvate kinase isoenzyme type M2 (FM2PK) and fecal osteoprotegerin (FOPG) as indicators of a number of disease characteristics. METHODS: The ImageKids study was a multicenter study designed to develop 2 magnetic resonance enterography-based measures for children with Crohn disease (6-18 years old). All patients underwent magnetic resonance enterography, a complete ileocolonoscopic evaluation and provided a fecal sample. Fecal samples were assay for FC, FA12, FM2PK, and FOPG by ELISA. RESULTS: One-hundred fifty-six children provided 190 fecal samples. Median (interquartile range) for fecal makers were FC, 602 (181-1185) μg/g; FA12, 21 (3-109) μg/g; FM2PK, 16 (2-20) U/mL; and FOPG, 125 (125-312) μg/g. All markers correlated with simple endoscopic severity index for Crohn disease and with other constructs of disease activity, but FC had the highest overall correlations. FA12, however, predicted mucosal healing with significantly higher specificity (87% vs 70%, P = 0.004) and equivalent sensitivity (91% vs 90%) compared to FC. CONCLUSION: This study has confirmed that FC is useful, and overall best, marker to monitor mucosal inflammation in inflammatory bowel disease. FA12, however, appears to be a more suitable maker for prediction of mucosal healing in children.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".