Weight of Evidence Approach for Skin Sensitization Potency Categorization of Fragrance Ingredients
Bibliographic record
Abstract
BACKGROUND: Reliable human potency data are necessary for conducting quantitative risk assessments, as well as development and validation of new nonanimal methods for skin sensitization assessments. Previously, human skin sensitization potency of fragrance materials was derived primarily from human data or the local lymph node assay. OBJECTIVES: This study aimed to define skin sensitization potency of fragrance materials via weight of evidence approach, incorporating all available human, animal, in vitro, in chemico, and in silico data. METHODS: All available data on 106 fragrance materials were considered to assign each material into 1 of the 6 defined potency categories (extreme, strong, moderate, weak, very weak, and nonsensitizer). RESULTS: None of the 106 materials were considered an extreme sensitizer, whereas a total of 6, 23, 41, and 26 materials were categorized as strong, moderate, weak, and very weak sensitizers, respectively. Ten materials lacked evidence for the induction of skin sensitization. CONCLUSIONS: Skin sensitization potency categorization of the 106 fragrance materials based on the described weight of evidence approach can serve as a useful resource in evaluation of nonanimal methods, as well as in risk assessment.
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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 | 0.101 | 0.201 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.037 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".