The Validated Hypoallergenic Cosmetics Rating System: Its 30-Year Evolution and Effect on the Prevalence of Cosmetic Reactions
Bibliographic record
Abstract
BACKGROUND: The validated hypoallergenic (vh) rating system was initiated in 1988 to try to objectively validate the "hypoallergenic" claim in cosmetics. OBJECTIVES: To show how the system rates cosmetic hypoallergenicity and to compare the prevalence of cosmetic contact dermatitis (CCD) among users of regular cosmetics versus cosmetics with high VH numbers. METHODS: (1) Made a VH list based on top allergens from patch-test results published by the North American Contact Dermatitis Group (NACDG) and the European Surveillance System on Contact Allergies (ESSCA); (2) reviewed global regulatory, cosmetic, drug, packaging, and manufacturing practices to show how allergens may contaminate products; (3) compared cosmetic ingredients lists against the VH list to obtain the VH rating (the more allergens absent, the higher the VH rating); and (4) obtained CCD prevalence among users of regular cosmetics versus users of cosmetics with high VH ratings. RESULTS: (1) Two VH lists (1988, 2003) included only cosmetic allergens in the NACDG surveys, the third (2007) included cosmetic and potential contaminant noncosmetic allergens, and the fourth (2010) adds ESSCA patch-test surveys. (2) CCD prevalence is 0.05 to 0.12% (average, 0.08%) among users of cosmetics with high VH ratings versus 2.4 to 36.3% among users of regular cosmetics. CONCLUSION: The VH rating system is shown to objectively validate the hypoallergenic cosmetics claim.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".