Prevalence of Preservatives Across All Product Types in the Contact Allergen Management Program
Bibliographic record
Abstract
BACKGROUND: Preservatives are known causes of allergic contact dermatitis. OBJECTIVE: The aim of this study was to determine the prevalence of preservatives in each product category in the Contact Allergen Management Program and compare prevalence with reported rates of allergic contact dermatitis. METHODS: Contact Allergen Management Program product information was queried based on the 53 approved preservatives for cosmetic products by the European Union and Association of Southeast Asian Nations plus 5 additional preservatives used in US products. RESULTS: Phenoxyethanol and parabens were the most common preservatives with 23.9% of products containing phenoxyethanol and 20.75% of products containing parabens. Methylisothiazolinone (MI) was found in 12.9% of products, most commonly in hair care and household products. Preservatives like MI and methylchloroisothiazolinone (MCI) that are both common in products and have a high incidence of allergic contact dermatitis are of greatest concern as contact allergy hazards. Phenoxyethanol and parabens are common and have weak sensitizing power, making them preferred preservatives. CONCLUSIONS: Evaluating the prevalence of preservatives provides important information on allergen exposures. Using current positive reaction rates, the risk of sensitization to a given preservative can be more accurately estimated and may affect the use of certain preservatives by industry in the future.
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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 | 0.002 |
| 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 | 0.000 |
| 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".