Natural Is Not Always Better: The Prevalence of Allergenic Ingredients in “Clean” Beauty Products
Bibliographic record
Abstract
BACKGROUND: Consumers seek "clean" and "natural" products based on their perception of safety. However, there is no standard, scientific basis, or regulatory oversight in the marketing or ingredient use for "clean" products. OBJECTIVE: The aim of this study was to determine the prevalence of allergenic ingredients in "clean" products. METHODS: Target and Walgreens Web sites were queried for "clean" products with inclusion of 1470 products. Ingredient lists were analyzed for potential allergens. Analysis included descriptive statistics and χ2 test. RESULTS: The most common allergens were fragrances/botanicals (1218/1470, 82.9%), phenoxyethanol (591/1470, 40.2%), tocopherol (545/1470, 40.2%), benzoic acid and benzoates (434/1470, 29.5%), propylene glycol (369/1470, 25.1%), alkyl glucosides (305/1470, 20.7%), ethylhexylglycerin (304/1470, 20.7%), cetyl alcohol (282/1470, 19.2%), cocamidopropyl betaine (258/1470, 17.6%), and benzyl alcohol (232/1470, 15.8%). Among fragrances/botanicals, the most common ingredients found were fragrance/perfume/aroma (911/1470, 68.2%), citrus derivatives (375/1470, 25.5%), linalool (305/1470, 20.7%), limonene (279/1470, 19.0%), and benzyl alcohol (231/1470, 15.7%). A total of 93.8% of the products (1379/1470) contained at least 1 potential allergen. CONCLUSIONS: Most "clean" products contain a potential allergen, predominately fragrances and botanicals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".