Moisturizers: A Comparison Based on Allergens and Economic Value
Bibliographic record
Abstract
BACKGROUND: The economic burden of cosmetics, such as moisturizers, has been increasing. Despite the high price of some market moisturizers, there have been no studies evaluating the allergenicity of these products. OBJECTIVE: The aim of this study was to evaluate the potential allergens within moisturizers based on economic value, by analyzing the substances found in moisturizers available online at the largest drugstore chain-CVS Health (CVS Health, Woonsocket, RI). METHODS: In this cross-sectional study, ingredients found in 50 expensive and 50 inexpensive moisturizers were matched with sensitizers within the Core Allergen Series published by the American Contact Dermatitis Society and the North American Contact Dermatitis Group. Student t test was used to compare the mean number of allergens present in each group. A χ test or Fisher exact test, where necessary, was used to compare the rates of specific allergen groups between the expensive and inexpensive products. RESULTS: Twenty-six allergenic substances were present overall in the 100 total products surveyed. The expensive moisturizers averaged significantly more allergens per product (8.28 vs 5.60, P = 0.003) than the inexpensive products. CONCLUSIONS: The sensitizing potential of expensive moisturizers may be higher than that of inexpensive moisturizers. Physicians may counsel cosmetic-induced allergic contact dermatitis (ACD) patients that monetary value is not a suitable proxy for evaluating the risk of ACD.
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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".