Comparison of Fecal Calprotectin Methods for Predicting Relapse of Pediatric Inflammatory Bowel Disease
Bibliographic record
Abstract
Background. Pediatric inflammatory bowel disease (IBD) is on the rise worldwide. Endoscopies are necessary for IBD assessment but are invasive, expensive, and inconvenient. Recently, fecal calprotectin (FCal) was proposed as a noninvasive and specific marker of gut inflammation. We evaluated the analytical performance of three FCal assays and their clinical performance in predicting relapse in pediatric IBD. Methods. This study used 40 pediatric IBD and 40 random non-IBD patients’ fecal samples. Two automated ELISAs (Bühlmann and PhiCal® Calprotectin-EIA) and an EliA (Phadia 250 EliA-Calprotectin) were used to evaluate the analytical performance. The clinical performance was assessed by PhiCal Calprotectin-EIA, EliA-Calprotectin, and Bühlmann immunochromatographic point-of-care test (POCT). Results. All assays displayed acceptable analytical performance below and above the medical decision cut-off [imprecision (CV < 10% intra-assay; <15% interassay); linearity (overall mean % deviation < 16.5%)]. The agreement with PhiCal Calprotectin-EIA was 100% and 78.6% for Bühlmann (95% CI, 87.5–100; Kappa: 1) and EliA-Calprotectin (95% CI, 60.5–89.8; Kappa: 0.32), respectively, and 63.6% between Bühlmann and EliA-Calprotectin (95% CI, 46.6–77.8; Kappa: 0.16). All assays evaluated had similar clinical performance [AUC: 0.84 (EliA-Calprotectin); 0.83 (POCT and PhiCal Calprotectin-EIA)]. Conclusion. FCal levels determined using the same method and assay together with clinical history would be a noninvasive and useful tool in monitoring pediatric IBD.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".