Relevance of Positive Patch-Test Reactions to Fragrance Mix
Bibliographic record
Abstract
BACKGROUND: Fragrances are an important cause of allergic contact dermatitis. We presume that the traditional fragrance mix (FM) detects 70 to 80% of fragrance-allergic patients. FM has an irritant potential. Weak positive reactions may have a greater chance of being irrelevant than strong reactions. OBJECTIVE: To improve the appraisal of FM patch-test reactions, we studied the relevance of reactions of different strength. We also studied the predictive value of the following on the relevance of the initial FM patch-test results: patch-test results of a repeated FM test; results of patch tests with balsam of Peru, colophony, and ingredients of the mix; and (history of) atopic dermatitis. METHODS: One hundred thirty-eight patients who had doubtful positive (?+) or positive (+ to +++) reactions were included in the study. We determined relevance by history taking, location and course of the dermatitis, and additional patch testing. Patients were retested with FM and with each ingredient separately. RESULTS: The relevance of reactions to FM increases with the strength of the reactions. Predictors of relevance are the results of retesting with FM, the results of tests with the ingredients, and a history and/or present symptoms of atopic dermatitis. CONCLUSION: Retesting with FM and its ingredients may add to the benefit of patch testing.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".