Patch Test Reactions Associated with Topical Medications: A Retrospective Analysis of the North American Contact Dermatitis Group Data (2001–2018)
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Topical medications may lead to allergic contact dermatitis. This study characterized positive patch test reactions associated with medications in patients evaluated by the North American Contact Dermatitis Group (NACDG). METHODS: This study is a retrospective analysis of the NACDG data (2001-2018). Patients with at least 1 positive patch test reaction associated with a medication source were included. Allergens, reaction characteristics, clinical relevance, and source details were tabulated. RESULTS: Of 43,722 patients, 6374 (14.6%) had positive allergic patch test reactions associated with 1 or more topical medication sources. Patients with versus without allergic reactions to medications were more likely to be older than 40 years (P < 0.0001) and/or have primary sites of dermatitis on the legs, anal/genital region, or trunk (P < 0.0001). There were 8787 reactions to NACDG allergens; the most common were neomycin (29.4%), bacitracin (29.1%), propylene glycol 100% (10.6%), tixocortol-17-pivalate (10.0%), lidocaine (7.9%), budesonide (4.9%), and dibucaine (4.4%). Propylene glycol 100% was the most common inactive ingredient (10.6%). Current relevance was present in 61.0%. A total of 6.5% of the individuals with medication allergy would have had 1 or more positive patch test reactions missed if only tested to the NACDG screening series. CONCLUSIONS: Positive patch test reactions associated with topical medications were common (14.6%), and most were clinically relevant. Patients with topical medication allergy were twice as likely to have anal/genital involvement. Active ingredients, especially neomycin, bacitracin, and tixocortol-17-pivalate, were frequent culprits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".