Risk Assessment of the Skin Sensitization Induction Potential of Kathon CG in Rinse-off and Leave-on Personal Care and Cosmetic Products
Bibliographic record
Abstract
BACKGROUND: Kathon CG is a commonly used cosmetic-grade preservative that contains active ingredients methylchloroisothiazolinone (MCI) and methylisothiazolinone (MI). OBJECTIVE: The aim of the study was to perform a skin sensitization induction risk assessment of daily exposure to Kathon CG after use of various personal care and cosmetic products. METHODS: We calculated an estimated daily consumer exposure level for rinse-off and leave-on products using the amount of product applied per application, number of applications per day, a retention factor, the MCI/MI concentration, and body surface area values. We assumed that the products contained the maximum recommended safe concentration of MCI/MI: 15 ppm in rinse-off products and 7.5 ppm in leave-on products. We compared estimated consumer exposure levels with the no expected sensitization induction level for MCI/MI and applied sensitization assessment factors to calculate product-specific margins of safety (MOSs). CONCLUSIONS: The MOSs for rinse-off products ranged from 5 to 63, whereas the MOSs for leave-on products ranged from 0.03 to 1.49. Overall, our results provide evidence that some leave-on products containing the maximum recommended safe concentration of Kathon CG may increase the risk of sensitization induction due to exposure to MCI/MI. In contrast, rinse-off products were not associated with a potential increased risk of skin sensitization induction.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".