Allergic contact dermatitis from essential oil in consumer products: Mode of uses and value of patch tests with an essential oil series. Results of a French study of the DAG (Dermato‐Allergology group of the French Society of Dermatology)
Bibliographic record
Abstract
OBJECTIVE: To analyse the clinical characteristics and sensitivity of an essential oil patch test series (EOS) in patients sensitized to their own essential oils (EOs). METHOD: We analysed the clinical data and patch test results obtained with the European baseline series (BSE) and an EOS, as well as the mode of use of EOs, through a questionnaire included in the patient file. RESULTS: The study included 42 patients (79% women, average age 50 years) with allergic contact dermatitis (ACD), 8 patients required hospitalization. All patients were sensitized to the EO they used, primarily lavender (Lavandula augustifolia, 8000-28-0), tea tree (Melaleuca alternifolia leaf oil, 68647-73-4), ravintsara (Cinnamomum camphora oil, 92201-50-8), and 2 cases were attributed to helichrysum (helichrysum italicum flower absolute, 90045-56-0). 71% had positive patch tests to fragrance mix I or II, 9 only to the EOS and 4 only with their personal EO. Interestingly, 40% of patients did not spontaneously mention the use of EOs, and only 33% received advice on their use at the time of purchase. CONCLUSION: Patch tests with the BSE, limonene and linalool HP, and oxidized tea tree oil is sufficient to detect most EO-sensitized patients. The most important is to test the patient's own used EOs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".