Prevention and management of allergic reactions to food in child care centers and schools: Practice guidelines
Bibliographic record
Abstract
Food allergy management in child care centers and schools is a controversial topic, for which evidence-based guidance is needed. Following the Grading of Recommendations Assessment, Development, and Evaluation approach, we conducted systematic literature reviews of the anticipated health effects of selected interventions for managing food allergy in child care centers and schools; we compiled data about the costs, feasibility, acceptability, and effects on health equity of the selected interventions; and we developed the following conditional recommendations: we suggest that child care centers and schools implement allergy training and action plans; we suggest that they use epinephrine (adrenaline) to treat suspected anaphylaxis; we suggest that they stock unassigned epinephrine autoinjectors, instead of requiring students to supply their own personal autoinjectors to be stored on site for designated at-school use; and we suggest that they do not implement site-wide food prohibitions (eg, "nut-free" schools) or allergen-restricted zones (eg, "milk-free" tables), except in the special circumstances identified in this document. The recommendations are labeled "conditional" due to the low quality of available evidence. More research is needed to determine with greater certainty which interventions are likely to be the most beneficial. Policymakers might need to adapt the recommendations to fit local circumstances.
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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 | 0.015 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".