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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".