Quantitative Risk Assessment of Contact Sensitization: Clinical Data to Assess Utility of the Model
Bibliographic record
Abstract
BACKGROUND: Contact hypersensitivity quantitative risk assessment (QRA) for fragrance ingredients is being used to establish new international standards for all fragrance ingredients that are potential skin sensitizers. OBJECTIVE: The objective was to evaluate the retrospective clinical data on three fragrance ingredients in order to provide a practical assessment of the predictive value of the QRA approach. It is important to have data to assess that the methodology provides a robust approach for primary prevention of contact sensitization induction for fragrance ingredients identified as potential sensitizers. METHODS: This article reviews clinical data for three fragrance ingredients-cinnamic aldehyde, citral, and isoeugenol-to assess the utility of the QRA approach for fragrance ingredients. RESULTS: This assessment suggests that had the QRA approach been available at the time standards were established for these fragrance ingredients, the clinical response might have been noticeably improved. Prospectively, with the establishment of QRA-derived standards, there should be a continued downward trend in patch test-positive rates for cinnamic aldehyde, citral, and isoeugenol over time. CONCLUSION: While it is recognized that the availability of retrospective data is limited, a longitudinal review of these data gives confidence that the QRA approach should be an effective tool for primary prevention. This study also highlights the importance of continued active monitoring of clinical patch-test data for fragrance ingredients.
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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 | 0.032 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".