Insights into self-reported food allergies in Romanian schoolchildren
Bibliographic record
Abstract
The prevalence of food allergy (FA) varies worldwide with an increasing rate in the last decades. Data of self-reported FA have been recorded by most European countries, the US, Canada and Australia, but not Romania. The aim of this study is to analyze the prevalence and severity of FA and to assess the extent of information the medical and teaching staff in schools have on students' medical history. Methods: A cross-sectional survey was performed in schoolchildren from Cluj-Napoca, Romania, using an online questionnaire delivered to their parents. Results and conclusions: Seven hundred and eight individuals completed the entire questionnaire. The prevalence of self-reported FA was 8.9%, 28.6% presented food-induced angioedema and 38.1% required ER presentation. Cow milk (36.5%), egg (9.5%), strawberry (20.6%) and nuts (2.7%)were the most frequent culprit foods. The lack of an appropriate and accurate communication with the medical and teaching staff in the school suggest the requirement for further measures for parents and children educations regarding food allergy detection and management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".