Allergen Content of Best-Selling Ethnic Versus Nonethnic Shampoos, Conditioners, and Styling Products
Bibliographic record
Abstract
BACKGROUND: Hair products are a potential cause of allergic contact dermatitis. There are limited data on the allergen content of ethnic hair products. OBJECTIVE: To identify allergens unique to ethnic hair products (shampoos, conditioners, styling products) and provide a resource for low allergen hair care products for patients with ethnic hair types. METHODS: The top 100 best-selling shampoos, conditioners, and styling products for ethnic and nonethnic hair products were determined from 3 major online retailers (Walmart, Target, Walgreens). Allergen was defined as presence on the 2017 American Contact Dermatitis Society Core 80 allergen list. RESULTS: The 2017 American Contact Dermatitis Society Core 80 allergens were tabulated for ethnic and nonethnic shampoos, conditioners, and styling products. A list of low-allergen shampoos, conditioners, and styling products was identified. Fragrance was the most common allergen for ethnic shampoos, conditioners, and styling products. Other notable allergens included methylchloroisothiazolinone/methylisothiazolinone, formaldehyde releasers, cetyl steryl alcohol, tocopherol, decyl glucoside, sodium benzoate, and phenoxyethanol. CONCLUSIONS: This study identifies important differences in allergens found in products marketed for ethnic hair compared with those marketed for nonethnic hair.
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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) | 0.005 | 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".