Occupational Contact Dermatitis in Dental Personnel: A Retrospective Analysis of the North American Contact Dermatitis Group Data, 2001 to 2018
Bibliographic record
Abstract
BACKGROUND: Dental personnel are at risk of developing occupational contact dermatitis. OBJECTIVES: The aims of the study were to determine prevalence of occupational contact dermatitis in dental personnel referred for patch testing and to characterize relevant allergens and sources. METHODS: The study used a retrospective, cross-sectional analysis of the North American Contact Dermatitis Group (NACDG) data, 2001-2018. RESULTS: Of 41,109 patients, 585 (1.4%) were dental personnel. Dental personnel were significantly more likely than nondental personnel to be female (75.7% vs 67.4%, P < 0.0001), have occupationally related dermatitis (35.7% vs 11.5%, P < 0.0001), and/or have primary hand involvement (48.6% vs 22.5%, P < 0.0001). More than one quarter of dental personnel (62/585, 27.7%) had 1 or more occupationally related allergic patch test reaction(s). There were 249 occupationally related reactions to NACDG screening allergens, most commonly glutaraldehyde (18.1%), thiuram mix (16.1%), and carba mix (14.1%). The most common sources of NACDG screening allergens were gloves (30.7%), dental materials (26.6%), and sterilizing solutions (13.1%). Seventy-three dental personnel (12.5%) had 1 or more positive patch test reactions to occupationally related allergen(s)/substances not on the screening series. Occupationally related irritant contact dermatitis was identified in 22.2% (n = 130) of dental personnel, most commonly to nonskin soaps/detergents/disinfectants (32.0%). CONCLUSIONS: Occupational contact dermatitis is common in dental personnel referred for patch testing. Comprehensive testing beyond screening series is important in these patients.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".