Peanut cross-contamination in randomly selected baked goods
Bibliographic record
Abstract
BACKGROUND: The current standard of care for managing peanut allergy includes avoidance of peanut and use of injectable epinephrine; however, strict avoidance is difficult and accidental ingestion is common with potentially serious consequences. Despite vigilance and efforts to minimize the risk of accidental exposure, peanut protein cross-contamination continues to occur in a variety of foods, including baked goods. OBJECTIVE: To assess and quantify the presence of peanut protein contamination in certain baked goods. METHODS: Randomly selected baked goods were collected from bakeries in the New York and Miami metropolitan areas that sold a variety of ethnic cuisines. A second set of samples from the same bakeries was collected at least 1 week after to evaluate between-batch variability. Samples were sent to the Food Allergy Research and Resource Program to analyze peanut contamination by enzyme-linked immunosorbent assay. Consumption estimates were based on 2003 to 2010 National Health and Nutrition Examination Survey survey data. RESULTS: Of 154 samples from 18 bakeries, 4 (2.6%) had detectable peanut contamination with peanut protein levels ranging from 0.1 mg/100 g to 650 mg/100 g. Consumption estimates for single occasion ingestion of a contaminated item ranged from 0.07 mg to 832 mg of peanut protein. CONCLUSION: In this study, unintended peanut protein was present in a small, but not insignificant, proportion of baked goods, with the potential to trigger a reaction in individuals with peanut allergy. Some products contained high levels of unintended peanut protein. The current data support the potential for accidental exposure to peanut protein with its associated risk.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".