Prevalence of reported food allergies in Brazilian preschoolers living in a small Brazilian city
Bibliographic record
Abstract
INTRODUCTION: Although the prevalence of allergic diseases, including food allergies, has increased over recent decades, relevant information on this topic is still lacking, particularly in younger children living in small cities. OBJECTIVE: To investigate the prevalence of reported food allergies in preschoolers in Limoeiro/Pernambuco, Brazil. METHODS: This was a cross-sectional study with preschoolers. Parents/guardians of all preschoolers enrolled at municipal schools between March and June 2019 (total of 619) were invited to complete a screening questionnaire (total of 619). Another 151 questionnaires were applied on the streets of the town. For positive responses, a second, more detailed and validated questionnaire was completed. RESULTS: A total of 412 questionnaires were returned, of which, 47 presented a positive response to food allergies and only 29 (7.04%) identified a particular food. The most frequently reported food items were shrimp, mollusks, pork, fruit and milk. Of the 29 who identified foods, 22 responded to the detailed questionnaire, resulting in only 4 (0.97%) positive responses. Of these, two were later discarded through clinical examinations and an open oral provocation test, resulting in a final prevalence of 0.48% of confirmed food allergies. CONCLUSION: The prevalence of reported food allergies was lower than that described in previous studies. The most commonly mentioned foods were shrimp, mollusks and pork, with more reports of multiple food allergies, even in children who had never been previously exposed to these possible allergens, which highlights the relevance of perception in reported food allergy studies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".