Allergic Contact Dermatitis to Linalool Hydroperoxides: Pitfalls in the Diagnostic Process—Findings from a Repeated Open Application Test Study
Bibliographic record
Abstract
Abstract: Background: Increasing trends of oxidized linalool contact allergy have been reported. However, the impact of reactivity and dose in eliciting allergic contact Dermatitis caused by linalool hydroperoxides is insufficiently investigated. Objectives: To perform repeated open application tests (ROATs) using the real-world concentrations of linalool hydroperoxides in patients and control participants. Materials and Methods: Patients who previously had a positive (patients) and a negative (controls) patch test reaction to linalool hydroperoxides 1.0% in petrolatum were patch tested with a dilution series of linalool hydroperoxides preparations and asked to perform ROAT twice daily with 3 concentrations of linalool hydroperoxides creams and a negative control cream for 28 days. The creams contain 44, 140, and 440 PPM of linalool hydroperoxides, representing real-world doses reported in consumer products. Results: Of all 47 participants, 31 were linalool hydroperoxides contact allergy patients, and 16 were controls. One patient had a positive ROAT reaction in the area where cream at the highest concentration of linalool hydroperoxides was applied for 28 days. Conclusions: Repeated exposure to creams containing linalool hydroperoxides at real-life concentrations could rarely elicit an allergic reaction on intact skin after 4 weeks.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".