New causes of occupational allergic contact dermatitis
Bibliographic record
Abstract
PURPOSE OF REVIEW: Occupational allergic contact dermatitis (OACD) is an important work-related skin disease. Information about the causative agents comes from many sources, including patch test databases, registries, case series and case reports. This review summarizes new information about common causative allergens and diagnosis. RECENT FINDINGS: Common causes of OACD include rubber components, epoxies and preservatives. New exposure sources for these allergens continue to be described. Often these exposure sources are related to the changing world around us, such as allergens related to smartphones and technology, and personal protective equipment-related exposures during the COVID-19 pandemic. New allergens are also being described, some of which are related to known allergens (e.g. a new epoxy or acrylate component).Accurate diagnosis is critical to effective management of OACD, which may include removing the worker from exposure to the causative allergen. Safety data sheets may not contain complete information and patch testing with specialized series of allergens and workplace materials may be necessary. SUMMARY: This review provides current evidence about causes of OACD and important aspects of diagnosis. This is important for clinical practice to ensure cases of OACD are not missed.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".