Contact Dermatitis in Construction Workers in Northeastern Italian Patch Test Database between 1996 and 2016
Bibliographic record
Abstract
BACKGROUND: Contact dermatitis in construction workers (CWs) is frequent due to the widespread exposure to sensitizing substances and irritating agents and the wet and cold working conditions. OBJECTIVE: Our objectives were to evaluate contact dermatitis characteristics among CWs who underwent patch test in northeastern Italy and to identify related allergens. METHODS: Seven hundred ninety-five CWs were studied and their data were compared to 2.099 male white-collar workers. The associations between patch test results and occupations were assessed by multivariate logistic regression analysis. Incidence data were calculated from 1996 to 2016. RESULTS: CWs with confirmed occupational dermatitis presented an increased risk to be sensitized to potassium dichromate [OR 3.1 (95%IC 2.0-4.8)], to thiurams [OR 8 .6 (95%IC 4.0-18.4)], and to epoxy resins [OR 12.7 (95%IC 6.1-26.4)]. Sensitization to chromate decreased significantly after 2004, following EU regulation of chromate content in concrete, while sensitization to epoxy resins and thiurams increased. The overall incidence of occupational contact dermatitis in CWs decreased significantly. CONCLUSION: Our study demonstrated the effectiveness of EU regulations in reducing chromate sensitization in CWs and the overall incidence of occupational contact dermatitis. However, sensitization to other haptens is increasing, though improvement of protective measures is compulsory.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".