Can my child with IgE‐mediated peanut allergy introduce foods labeled with “may contain traces”?
Bibliographic record
Abstract
Peanut IgE-mediated food allergy is one of the most common food allergies in children with a prevalence that has increased in the past decades in Westernized countries. Peanut allergies can trigger severe reactions and usually persist over time. Peanut-allergic children and their families are often confronted to processed foods with precautionary allergen labeling (PAL) such as "may contain traces of peanuts," which are frequently used by the food industry. Patients are generally confused as to whether eating such foods entails a risk of allergic reaction, which can ultimately lead to dietary restrictions and decreased quality of life. Thus, guidance toward eviction of foods with PALs such as "may contain traces of peanuts" is a recurring problem that peanut-allergic patients address during pediatric allergy consultations with varying attitudes among allergists. Many studies have evaluated peanut contamination in foods with PALs, with generally less than 10% of foods containing detectable levels of peanuts, albeit heterogeneous amounts, with in rare occasions levels that could trigger allergic reactions in certain patients. The risk of reacting to foods with traces varies significantly with threshold, with patients with the lowest reaction thresholds at highest risk, and a dramatic reduction of risk as threshold increases. Thus, risk stratification based on individual reaction threshold may help stratify patients' risk of reacting to foods with PAL. In clinical practice, a single-dose 30 mg peanut protein oral food challenge may be an option to stratify peanut-allergic patients' risk when introducing foods with PAL, as illustrated by three clinical cases.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".