Irritant and Sensitizing Potential of Eight Surfactants Commonly Used in Skin Cleansers: An Evaluation of 105 Patients
Bibliographic record
Abstract
BACKGROUND: Irritation from surfactants contained in detergents is a frequent adverse reaction to cosmetics. Sensitization to surfactants is also possible. In the literature, comparative studies about irritant and sensitizing potential of different surfactants are heterogeneous and inconclusive about the best molecules to use. OBJECTIVES: We compared the irritant and sensitizing potential of some surfactants that are usual components in marketed synthetic detergents (syndets) to obtain practical information regarding commonly used detergents. METHODS: We patch-tested eight surfactants of the different types (anionic, cationic, amphoteric, and non-ionic) in 105 patients. Assessment of allergic reactions of tested surfactants was carried out in accordance with the recommendations of the International Contact Dermatitis Research Group; assessment of irritant power followed the amended Draize classification. RESULTS: None of the eight surfactants in our series gave positive allergic reactions. Only cocamidopropyl betaine from the Italian standard (Società Italiana di Dermatologia Allergologica, Professionale e Ambientale [SIDAPA]) series gave five positive reactions among 105 patients. None of the eight studied surfactants induced skin irritation. The most tolerated are two new mild anionics (sodium cocoyl glutamate and sodium lauroyl oat amino acids) and an amphoteric agent (disodium cocoamphodiacetate). CONCLUSION: From this study, we deduce that cosmetic companies' efforts to search for and market new products with very mild surfactants have been generally successful.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".