Towards a common approach for managing food allergy and serious allergic reactions (anaphylaxis) at school. GA<sup>2</sup>LEN and EFA consensus statement
Bibliographic record
Abstract
LEN and EFA propose minimum specifications for all industrialised countries/regions to work towards to support students with food allergies in educational settings. We reviewed research and legislation and gained feedback from over 100 patient and professional groups. We built shared expectations around: 1. training all school staff about what food allergy is, the symptoms of allergic reactions, what to do in an emergency, and when and how to use and store devices that laypeople can use to administer adrenaline (epinephrine). 2. preventing allergic reactions by using clear labelling on school menus and prepacked and non-prepacked foods and regular cleaning where students eat. 3. preparing for serious allergic reactions, with written emergency action plans for every student with food allergies, legislation allowing schools to store adrenaline for anyone who needs it in an emergency (not just those prescribed it), and training and legal safeguards for staff administering adrenaline. 4. including affected students by discussing food allergy in the curriculum, raising awareness among all students and caregivers and reviewing school processes regularly. It is time for national and international action at the policy level. Patient groups, education networks and professional societies all play a role in campaigning for shared next steps.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.010 |
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".