Early Infant Feeding Practices as Possible Risk Factors for Immunoglobulin E-Mediated Food Allergies in Kuwait
Bibliographic record
Abstract
Objective. Early feeding and infant exposures have been suggested as potential risk factors for immunoglobulin E- (IgE-) mediated food allergy (FA). We aimed to evaluate the association between IgE-mediated FA in children and early exposures including the child’s nutritional status, breastfeeding and its duration, the age at which the solid food was first introduced, antibiotic exposure during the first year of life, and the child’s vitamin D status during infancy. Design. A case-control study. Setting and Subjects. Children aged 0–13 years were recruited from pediatric allergy and immunology clinics (PAICs) located at major government hospitals in Kuwait (total FA cases: n=100 ; boys = 67%), and healthy controls ( n=100 , boys 55%) were recruited from various vaccination units at primary healthcare centers. Results. Cow’s milk allergy was the most common type of FA. FA status was independently associated with the early exposures of exclusive breastfeeding (aOR = 15.55 (3.26–74.19), p=0.001 ), vitamin D deficiency or insufficiency during infancy (aOR = 5.42 (1.92–15.30), p=0.001 ), and antibiotic exposure during the first year of life (aOR = 5.00 (1.58–15.84), p=0.006 ). Conclusions. FA is highly prevalent among children in Kuwait, and our data indicate that early nutrition-related and antibiotic exposures are associated with FA risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".