Removal of peanut allergen Ara h 1 from common hospital surfaces, toys and books using standard cleaning methods
Bibliographic record
Abstract
BACKGROUND: In children, a diagnosis of peanut allergy causes concern about accidental exposure because even small amounts of peanut protein could trigger an allergic reaction. Contamination of toys, books or other items by peanut butter in areas where individuals have eaten may occur in hospital waiting rooms and cafeterias. It is not known if hospital cleaning wipes are effective in removing peanut allergen. OBJECTIVES: The purpose of this study was to determine whether cleaning peanut contaminated items with common household and hospital cleaning wipes would remove peanut allergen. METHODS: 5 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, a plastic doll, and a textured plastic ball, and 2.5 mL was applied to smooth and textured book covers. Samples for measurement of Ara h 1 were collected prior to the application of the peanut butter (baseline), and after cleaning with a common household wipe and two commercial hospital wipes. 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 each item. The samples were diluted 1:50 for testing. RESULTS: At baseline, there was no detectable Ara h 1 allergen on any item at baseline. Detectable Ara h 1 was detected on all products after applying peanut butter (range 1.2-19.0 micrograms/mL). After cleaning with any product, no Ara h 1 was detected on any item. CONCLUSIONS: Table surfaces, book covers and plastic toys can be cleaned to remove peanut allergen Ara h 1 using common household and hospital cleaning wipes. Regular cleaning of these products or cleaning prior to their use should be promoted to reduce the risk of accidental peanut exposure, especially in areas where they have been used by many children.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".