Patch Testing to <i>Mentha piperita</i> (Peppermint) Oil: The North American Contact Dermatitis Group Experience (2009–2020)
Bibliographic record
Abstract
Abstract: Background: Mentha piperita (MP; peppermint) oil has many commercial uses. Objective: To characterize the epidemiology of contact allergy to MP oil 2% petrolatum. Methods: Retrospective analysis of North American Contact Dermatitis Group data (2009–2020). Results: Of 28,128 patients tested to MP, 161 (0.6%) had an allergic reaction. Most allergic patients were female (77.0%) and/or over 40 years of age (71.4%). The most common anatomical sites of dermatitis included face (31.7%; of these, one-third specified lips), hands (17.4%), and scattered/generalized (18.6%). Nearly one-third (30.4%) of reactions were strong (++)/extreme (+++), and 80.1% were considered currently relevant. Common sources included oral hygiene preparations, foods, and lip products. Co-reaction with at least 1 of the other 19 fragrance/plant-related screening test preparations occurred in 82.6% (133/161) of MP-allergic patients, most commonly Cananga odorata oil (42.9%), fragrance mix I (41.0%), hydroperoxides of linalool (35.7%), Compositae mix (35.4%), Jasminum officinale oil (31.9%), Myroxylon pereirae (31.7%), and propolis (28.1%). Co-reaction with at least 1 of the 3 most commonly used fragrance screening allergens (fragrance mix I, fragrance mix II, and/or Myroxylon pereirae ) was 59.6%. Conclusions: Twelve-year prevalence of MP allergy was 0.6%. Approximately 40% of cases would have been missed if only fragrance screening allergens were tested.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".