Bibliographic record
Abstract
BACKGROUND: Allergic contact dermatitis commonly occurs on the face. Facial cleansing wipes may be an underrecognized source of allergens. OBJECTIVE: The aim of this study was to determine the frequency of potentially allergenic ingredients in facial wet wipes. METHODS: Ingredient lists from name brand and generic facial wipes from 4 large retailers were analyzed. RESULTS: In the 178 facial wipes examined, a total of 485 ingredients were identified (average, 16.7 ingredients per wipe). Excluding botanicals, the top 15 potentially allergenic ingredients were glycerin (64.0%), fragrance (63.5%), phenoxyethanol (53.9%), citric acid (51.1%), disodium EDTA (44.4%), sorbic acid derivatives (39.3%), tocopherol derivatives (38.8%), polyethylene glycol derivatives (32.6%), glyceryl stearate (31.5%), sodium citrate (29.8%), glucosides (27.5%), cetearyl alcohol (25.8%), propylene glycol (25.3%), sodium benzoate (24.2%), and ceteareth-20 (23.6%)/parabens (23.6%). Of note, methylisothiazolinone (2.2%) and methylchloroisothiazolinone (1.1%) were uncommon. The top potential allergens of botanical origin included Aloe barbadensis (41.0%), chamomile extracts (27.0%), tea extracts (21.3%), Cucumis sativus (20.2%), and Hamamelis virginiana (10.7%). CONCLUSIONS: Many potential allergens are present in facial wet wipes, including fragrances, preservatives, botanicals, glucosides, and propylene glycol.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.004 | 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".