Evaluating Copositivity Patterns in Fragrance/Botanical Patch Testing through Hierarchical Clustering and Network Analysis
Bibliographic record
Abstract
Abstract: Background: Fragrances/botanicals are ubiquitous allergens. Patients allergic to one fragrance/botanical are frequently sensitive to other fragrances/botanicals and are typically counseled to avoid all fragrances/botanicals. However, broad avoidance of all fragrances/botanicals may not be clinically necessary. Objectives: We examined copositivity patterns in fragrance/botanical patch testing. Methods: The Mayo Clinic patch test database was queried for pairwise copositivity rates for fragrances/botanicals between 1997 and 2022, representing a total of 43 allergens. Data analyzed included 4706 positive reactions out of 252,485 total patches applied to 15,864 patients. After background correction for general positivity, copositivity rates were organized through unsupervised hierarchical clustering to determine copositivity subgroups and then evaluated through network analysis. Results: After background correction, clustering revealed distinct copositivity subgroups: Fragrance Mix I– Myroxylon pereirae –limonene hydroperoxides–linalool hydroperoxides; Compositae Mix–sesquiterpene lactone–parthenolide; Fragrance Mix II–Lyral; lichen acid mix–treemoss extract; menthol– Mentha piperita ; narcissus–dandelion; and Santalum album –trans-anethole–tea tree–lemongrass–clove–turpentine– Rosa damascena – Lavandula – Geranium – Cananga odorata –neroli–bergamot. In addition, there were further isolated intergroup copositivity reactions seen in network analysis. Conclusions: Background correction followed by hierarchical clustering demonstrated the fragrance/botanical group can be divided into multiple copositivity subgroups. Combined with network analysis, patients with a positive patch test to one fragrance/botanical allergen may be preferentially guided to use specific other fragrances/botanicals.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".