Occupationally Related Nickel Reactions: A Retrospective Analysis of the North American Contact Dermatitis Group Data 1998–2016
Bibliographic record
Abstract
BACKGROUND: The epidemiology of nickel allergy in occupational settings is not well understood. OBJECTIVE: The aim of the study was to characterize occupationally related nickel allergy (ORNA). METHODS: This is a retrospective cross-sectional analysis of 44,378 patients patch tested by the North American Contact Dermatitis Group from 1998 to 2016. Characteristics of individuals with ORNA were compared with those with non-ORNA (NORNA). RESULTS: A total of 7928 (18.2%) individuals were positive to nickel sulfate 2.5%. Two hundred sixty-eight (3.4%) had ORNA. As compared with NORNA, ORNA was statistically associated with the male sex (41.0% vs 12.9%, P < 0.001), a diagnosis of irritant contact dermatitis (22.4% vs 12.0%, P < 0.001), and no history of eczema (81.7% vs 75.7%, P = 0.0217). The most common sites of ORNA dermatitis were hand (39.9%) and arm (18.1%), which were significantly more common than in NORNA (P < 0.0001). Sixteen industry categories and 22 occupation categories were identified for ORNA; the most common industries were durable goods manufacturing (24.6%) and personal services (15.7%), and the most frequent occupations were hairdressers/cosmetologists/barbers (14.3%), machine operators (9.3%), and health care workers (7.1%). Overall 30% of ORNA occupations were in metalworking. Of 215 ORNA sources identified, instruments/phones/other equipment (16.3%), vehicles/machinery (15.8%), and tools (15.3%) were the most common. CONCLUSIONS: Occupational nickel allergy is distinct from nonoccupational nickel allergy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".