Association of fecal calprotectin level with eosinophilic gastrointestinal disease in Iranian pediatrics
Bibliographic record
Abstract
INTRODUCTION: Fecal calprotectin (FC) is a noninvasive biomarker for assessing the inflammatory status of the gastrointestinal tract. The aim of this study was to determine the association between FC levels and Eosinophilic colitis (EC) before and after treatment in pediatrics. METHOD: In this cross-sectional study, 330 patients with rectorrhagia and FC levels > 200 μg/g were included in the study. Patients were then subjected to colonoscopy, and if 30 or more eosinophils were observed in the pathology of at least two parts of the colon, EC was diagnosed. Of the 330 patients included in the study, 14 patients were diagnosed as EC. Treatment included seven food elimination diet (food allergens) for 3 months. After 3 months, FC levels were repeated and colonoscopy was performed. RESULTS: The mean age of the children was 5.9 years. After the elimination diet, the number of eosinophils in all segments of colon significantly decreased (P < 0.001) and according to the pathology report, the number of eosinophils improved in 42.9% of patients. Also, the mean number of segments involved in the colon of patients was significantly decreased (P < 0.001). Mean FC levels were significantly decreased after 3 months (P < 0.001). The cut-off point of 114 μg/g of FC had sensitivity (75%), specificity (67%), positive predictive value (75%), negative predictive value (67%), accuracy (71.4%), and area under the ROC curve (0.708) acceptable in predicting EC. CONCLUSION: This study showed that FC levels can be elevated in patients with EC, which is easily corrected with a targeted elimination of food allergens.
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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 | 0.000 | 0.001 |
| 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".