Persistence of peanut allergen on a table surface
Bibliographic record
Abstract
BACKGROUND: A diagnosis of peanut allergy has a major impact on an individual's quality of life. Exposure to even small amounts of peanut can trigger serious reactions. Common cleaning agents can easily remove peanut allergen from surfaces such as table tops. Parents of children with peanut allergy frequently ask if peanut allergen can persist on surfaces if they have not been cleaned. OBJECTIVES: The purpose of this study was to determine the persistence of peanut allergen on a typical table surface over time. METHODS: Five mL of peanut butter was evenly smeared on a 12 inch by 12 inch (30.5 by 30.5 cm) square on a nonporous (laminated plastic) table surface. Five squares were prepared in the same manner. The table was kept in a regular hospital office at room temperature and ambient lighting. No cleaning occurred for 110 days. Samples were taken at regular intervals from different areas each time. A monoclonal-based ELISA for arachis hypogaea allergen 1 (Ara h 1), range of detection 1.95-2000 ng/mL, was used to assess peanut allergen on the table surface. RESULTS: At baseline, there was no detectable Ara h 1 allergen. Immediately post application and for 110 days of collecting, detectable Ara h 1 was found each time a sample was taken. There was no obvious allergen degradation over time. Active cleaning of the contaminated surface with a commercial cleaning wipe resulted in no detectable Ara h 1 allergen. CONCLUSIONS: Peanut allergen is very robust. Detectable Ara h 1 was present on the table surface for 110 days. Active cleaning of peanut contaminated surfaces easily removed peanut residue and allergen. Regular cleaning of surfaces before and after eating should be reinforced as a safety measure for all individuals with peanut allergy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".