Occupational Contact Dermatitis in North American Print Machine Operators Referred for Patch Testing: Retrospective Analysis of Cross-Sectional Data From the North American Contact Dermatitis Group 1998 to 2014
Bibliographic record
Abstract
BACKGROUND: Little is known about the epidemiology of contact dermatitis (CD) in print machine operators (PMOs). OBJECTIVE: The aims of this study were to estimate the prevalence of CD and characterize clinically relevant and occupationally related allergens among PMOs undergoing patch testing. METHODS: This was a retrospective cross-sectional analysis of the North American Contact Dermatitis Group data from 1998 to 2014. RESULTS: Of 39,332 patch-tested patients, 132 (0.3%) were PMOs. Among PMOs, most were male (75.0%) and white (92.4%). The majority were printing press operators (85.6%). The most frequent sites of dermatitis were hands (63.6%), arms (29.5%), and face/scalp (24.2%). More than half had an occupationally related skin condition (56.1%). Final diagnoses were most commonly allergic CD (58.3%) and irritant CD (33.3%). Cobalt (20.8%), carba mix (12.5%), thiuram mix (8.3%), and formaldehyde (8.3%) were the most frequent occupationally related allergens. The top allergen sources included inks (22.9%), gloves (20.8%), and coatings/dye/copy/photographic chemicals (14.6%). CONCLUSIONS: Allergic CD, irritant CD, and involvement of exposed body areas were common among PMOs. Common allergens included rubber accelerators, metals, and preservatives.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".