Acrylate Contact Allergy: Patient Characteristics and Evaluation of Screening Allergens
Bibliographic record
Abstract
BACKGROUND: Acrylates are present in a wide variety of products and cause occupational and non-occupational allergic contact dermatitis. There is no clear guidance from the literature as to which allergens should be used for patch-test screening for acrylates. OBJECTIVES: To characterize patients with contact allergy to acrylates and to evaluate the allergens used to screen for acrylate allergy. METHODS: Charts of patients visiting an outpatient contact dermatitis clinic from January 1998 to February 2008 were reviewed retrospectively. RESULTS: Forty-four patients were found to have contact allergy to acrylates. The most commonly positive allergens were hydroxyethyl methacrylate (HEMA), ethyl acrylate (EA), and methyl methacrylate (MMA). Thirty-two of the 44 positive patch-test results (73%) would have been discovered with the use of the two compounds (MMA, EA) in the North American Standard Series (Chemotechnique screening series), a commercially available screening series, while 12 were found through expanded patch testing. Artificial nails, dental materials, and adhesives were the most common exposures. Occupational relevance was found in 18 cases, including those of dental workers, assemblers, and aestheticians. CONCLUSIONS: Acrylates are an important cause of contact allergy. Screening series identify most cases of acrylate allergy but miss a substantial number. Clinicians should remain vigilant for acrylate allergy even if initial screening is negative.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".