Allergenic Ingredients in Personal Hygiene Wet Wipes
Bibliographic record
Abstract
BACKGROUND: Wet wipes are a significant allergen source for anogenital allergic contact dermatitis. OBJECTIVE: The aim of the study was to calculate the frequency of potentially allergenic ingredients in personal hygiene wet wipes. METHODS: Ingredient lists from brand name and generic personal hygiene wet wipes from 4 large retailers were compiled. RESULTS: In the 54 personal hygiene wet wipes evaluated, a total of 132 ingredients were identified (average of 11.9 ingredients per wipe). The most common ingredients were Aloe barbadensis (77.8%), citric acid (77.8%), fragrance (72.2%), sorbic acid derivatives (63.0%), tocopherol derivatives (63.0%), glycerin (59.3%), phenoxyethanol (55.6%), disodium cocoamphodiacetate (53.7%), disodium ethylenediaminetetraacetic acid (EDTA) (42.6%), propylene glycol (42.6%), iodopropynyl butylcarbamate (40.7%), chamomile extracts (38.9%), sodium benzoate (35.2%), bronopol (22.2%), sodium citrate (22.2%), lanolin derivatives (20.4%), parabens (20.4%), polyethylene glycol derivatives (18.5%), disodium phosphate (16.7%), dimethylol dimethyl hydantoin (DMDM) (14.8%), and cocamidopropyl propylene glycol (PG)-dimonium chloride phosphate (11.1%). Of note, methylisothiazolinone (5.6%) was uncommon; methylchloroisothiazolinone was not identified in the personal hygiene wet wipes examined. CONCLUSIONS: There are many potential allergens in personal hygiene wet wipes, especially fragrance and preservatives.
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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 | 0.003 | 0.002 |
| 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) | 0.003 | 0.001 |
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".