Contact Sensitization in the Cleaning Industry: Updated Analysis of Contact Allergy Surveillance Data of the Information Network of Departments of Dermatology
Bibliographic record
Abstract
Abstract: Background: Occupational contact allergy is common in the cleaning industry. Objective: To identify common occupational sensitizers and analyze time trends in type IV sensitization in female cleaners. Methods: Data of 860 female cleaners with occupational dermatitis (OD) patch tested in 58 centers within the Information Network of Departments of Dermatology from 2010 to 2022 were analyzed. The comparison group consisted of 966 female cleaners without OD. Statistical analyses included 95% confidence intervals (CIs) and the exact Cochran–Armitage trend test. Results: Among 860 female cleaners with OD, 729 (84.8%) had hand dermatitis. Most frequent final diagnoses were irritant contact dermatitis (32.8%) and allergic contact dermatitis (25.3%). Compared to female cleaners without OD, higher sensitization rates were found for rubber additives (thiuram mix [8.6%, 95% CI: 6.5–10.7], zinc diethyldithiocarbamate [2.0%, 95% CI: 0.9–3.1], mercapto mix [1.3%, 95% CI: 0.4–2.1]), formaldehyde (2.3%, 95% CI: 1.0–3.6), and hydroxyisohexyl 3-cyclohexene carboxaldehyde (HICC) (3.0%, 95% CI: 1.5–4.6). Glutaraldehyde sensitization showed a decreasing time trend over the study period and should be further monitored. Conclusions: Rubber additives, preservatives/disinfectants (aldehydes), and HICC are occupationally relevant sensitizers in female cleaners, underscoring the need for targeted prevention strategies and continuous monitoring to protect their health.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".