Can we define a tolerable level of risk in food allergy? Report from a <scp>E</scp>uro<scp>P</scp>revall/<scp>UK F</scp>ood <scp>S</scp>tandards <scp>A</scp>gency workshop
Bibliographic record
Abstract
BACKGROUND: There is an emerging consensus that, as with other risks in society, zero risk for food-allergic people is not a realistic or attainable option. Food allergy challenge data and new risk assessment methods offer the opportunity to develop quantitative limits for unintended allergenic ingredients which can be used in risk-based approaches. However, a prerequisite to their application is defining a tolerable level of risk. This requires a value judgement and is ultimately a 'societal' decision that has to involve all relevant stakeholders. OBJECTIVE: The aim of the workshop was to bring together key representatives from the stakeholders (regulators, food industry, clinical researchers and patients), and for the first time ever discuss the definition of a tolerable level of risk with regard to allergic reactions to food. RESULTS: The discussions revealed a consensus that zero risk was not a realistic option and that it is essential to address the current lack of agreed action levels for cross-contamination with allergens if food allergen management practice is to be improved. The discussions also indicated that it was difficult to define and quantify a tolerable level of risk, although both the clinical and the industry groups tried to do so. A consensus emerged that doing nothing was not a viable option, and there was a strong desire to take action to improve the current situation. CONCLUSIONS AND CLINICAL RELEVANCE: Two concrete actions were suggested: (1) Action levels should be derived from the data currently available. Different scenarios should be examined and further developed in an iterative process. On the basis of this work, a tolerable level of risk should be proposed. (2) 'One-dose' clinical trial with a low challenge dose should be performed in multiple centres to provide additional information about the general applicability of dose-distribution models and help validate the threshold levels derived.
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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 | 0.107 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.013 | 0.021 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".