Can fecal calprotectin levels be used to monitor infant milk protein allergies?
Bibliographic record
Abstract
BACKGROUND: Milk protein allergy is one of the most common food allergies in infants. We aimed to test whether fecal calprotectin can be used to monitor food allergies in infants by comparing the fecal calprotectin levels in infants with a milk protein allergy before and after an intervention treatment. METHODS: The study was designed as a prospective case-control trial. Stool samples were collected at follow-up, and the concentration of fecal calprotectin was determined using an enzyme-linked immunosorbent assay. The infant's weight and length were measured. RESULTS: The allergic group comprised 90 milk-allergic infants (41 boys, 49 girls), and the nonallergic group comprised 90 nonallergic infants (51 boys, 39 girls). Compared with the fecal calprotectin level in the nonallergic group (median: 141 μg/g), that in the allergic group (median: 410 μg/g) was significantly higher (z = - 9.335, p < 0.001). After two dietary interventions and treatments, the fecal calprotectin levels of the infants with a milk protein allergy at the first (median: 253 μg/g) and second follow-up visits (median: 160 μg/g) were significantly lower than those before the intervention (z = - 7.884, p < 0.001 and z = - 8.239, p < 0.001, respectively). The growth index values (LAZ and WAZ) of the infants with a milk protein allergy at the first and second follow-up visits were significantly higher than those before dietary intervention (p < 0.05). Fecal calprotectin was negatively and significantly correlated with the WLZ and WAZ at the second follow-up visit (Spearman's rho = - 0.234, p = 0.01 and Spearman's rho = - 0.193, p = 0.03, respectively). CONCLUSION: The level of fecal calprotectin in infants with a milk protein allergy decreased after dietary intervention and seems to be a promising biological indicator for monitoring intestinal allergies.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".