Supplemental Patch Testing Identifies Allergens Missed by Standard Screening Series
Bibliographic record
Abstract
Abstract: Background: Supplemental patch testing is an adjunct to standard patch test screening series. Objective: To determine the demographics, characteristics, frequency, relevance, and interpretation of patch test reactions for supplemental patch testing. Methods: Retrospective study of patients tested 2017–2020 with North American Contact Dermatitis Group (NACDG) and supplemental screening series (Supplemental Series A [SSA], Supplemental Series B [SSB]). Demographics, characteristics, reaction strengths, relevance, and final interpretation were recorded. Results: Cohort included 791 patients; 73.5% female, 68.6% age >40 years. 74.1% were White, 15.2% Black, 5.7% Asian, and 1.5% Hispanic. The most common Dermatitis sites were scattered/generalized (27.2%), face (24.0%), and hands (23.5%). For 2017–2018 and 2019–2020, respectively, 82% (318/388) and 78.4% (316/403) had ≥1 “allergic” reaction. In addition, 13.5% (52/385) and 11.7% (47/403) had SSA reactions, and 38.1% (115/302) and 31.5% (101/321) had SSB reactions. In the 87 (2017–2018) and 99 (2019–2020) patients with negative NACDG testing, 17 (19.5%) and 12 (12.1%) had supplemental reactions. Of the 34 supplemental allergens with reaction frequency ≥1%, 58.8% (20/34) are not part of the American Contact Dermatitis Society 90 (2020) or NACDG 2021–2022 screening series. The highest frequency allergens from this group were dodecyl and octyl gallate, cinnamic alcohol, phenyl salicylate, hexahydro-1,3,5-tris-(2-hydroxyethyl) triazine, and abitol. Conclusions: Supplemental patch testing identifies additional relevant allergens in patients with suspected allergic contact Dermatitis.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".