Prevalence of food allergens sensitization and food allergies in a group of allergic Honduran children
Bibliographic record
Abstract
BACKGROUND: Food allergy is a public health problem that has increased in the last decade. Despite the increasing rates in children, quality data on the burden of these diseases is lacking particularly in developing countries. Honduras has no studies in pediatric patients. OBJECTIVES: The objective of this research was to identify the most common sensitization patterns to food through epicutaneous skin testing and food allergy rates in children and their correlation with common allergic diseases in a group of patients from Hospital of Pediatrics Maria. METHODS: Cross-sectional retrospective, descriptive study in which records and database of all allergic patients in the immunology outpatient clinic from Hospital of Pediatrics Maria were reviewed between the periods of January 2015 through June 2016. RESULTS: A total of 365 children were analyzed, the age of participants were in the range from 1 to 18 years, with an average of 9.8 years. Sensitization to food allergens were found in 23, and 58.3% were poly-sensitized. The most common food allergens that patients were sensitized to: milk 9.0%, eggs 6.9%, peanut 4.9% and pork meat 4.4%. Food allergy was confirmed via oral food challenged in 9.3% of the patients. The most frequent food allergies found were: cow's milk allergy 6%, hen's egg allergy 5.2% and wheat allergy 1.9%. CONCLUSIONS: Milk and egg were the most common a food allergens found in the population studied. Most of the patients were found to be poly-sensitized. The frequent food allergies confirmed via oral food challenge were cow's milk allergy, hen's egg allergy and wheat allergy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".