2‐Hydroxyethyl methacrylate (HEMA): A clinical review of contact allergy and allergic contact dermatitis—Part 1. Introduction, epidemiology, case series and case reports
Bibliographic record
Abstract
2-Hydroxyethyl methacrylate (HEMA) has been increasingly recognised as a contact allergen and was added to the European baseline series in 2019. In this article (2 parts), the results of an extensive literature review of the clinical aspects of contact allergy/allergic contact dermatitis to HEMA are presented. In part 1, the epidemiology of HEMA contact allergy is discussed and detailed information on published case series and case reports presented. HEMA is an important cause of contact allergy/allergic contact dermatitis in North America and Europe with recent prevalences of >3% in the USA + Canada and 1.5%-3.7% in Europe. Currently, most cases are caused by nail cosmetics, both in consumers and professional nail stylists. In our literature review, we have found 24 studies presenting case series of patients with allergic contact dermatitis attributed to HEMA and 168 case reports. However, the presence of HEMA in the products causing ACD was established in only a minority. Part 2 will discuss cross- and co-sensitisation, and other skin reactions to HEMA, will assess whether HEMA is the most frequent (meth)acrylate allergen and how sensitive HEMA as a screening agent is, investigate the presence of HEMA in commercial products and provide practical information on patch testing procedures.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".