Patch Test Sensitization to Compositae Mix, Sesquiterpene-Lactone Mix, Compositae Extracts, Laurel Leaf, Chlorophorin, Mansonone A, and Dimethoxydalbergione
Bibliographic record
Abstract
Background: Compositae mix and sesquiterpene-lactone (SL) mix are important patch test substances to show allergic contact dermatitis from various Compositae plants. Objectives: The aims of this study are to calculate the sensitization rates to Compositae mix and SL mix in an occupational dermatology clinic and to describe cases of active sensitization caused by patch testing with Compositae mix and SL mix. Methods: Conventional patch testing was performed. SL mix (0.1%) and Compositae mix (6% in petrolatum) were tested in a modified European standard series and a plant allergen series. Testing with other appropriate patch test series was also performed. Results: SL mix provoked 8 allergic patch test reactions (0.7%) in 1,076 patients, whereas Compositae mix was positive in 15 of 346 patients (4.2%). Three patients were actively sensitized to Compositae mix and 1 patient to SL mix. One patient was also sensitized to other plant allergens in a series of allergenic plant chemicals, namely to Mansonone A, an ortho-quinone; (R)-3,4-dimethoxydalbergione, a quinone; and Chlorophorin, a hydroxy stilbene. Allergic patch test reactions to laurel leaf were caused by cross-sensitization to SLs. Conclusion: Compositae mix seems to be a more important patch test substance than SL mix to detect allergic contact dermatitis to Compositae plants, but patch testing may sensitize. The concentration of the individual components of the Compositae mix should be adjusted so that the mix detects allergic patients but does not sensitize.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".