Piercing and Metal Sensitivity: Extended Analysis of the North American Contact Dermatitis Group Data, 2007–2014
Bibliographic record
Abstract
BACKGROUND: Body piercing provides a unique route of metal exposure. OBJECTIVE: The aim of this study was to update previous analyses using the North American Contact Dermatitis Group data comparing pierced and unpierced individuals. METHODS: This was a retrospective cross-sectional analysis of 17,912 patients patch tested by the North American Contact Dermatitis Group from 2007 to 2014 for demographics, positive reactions to metals (nickel, cobalt, chromium), and detailed analysis of nickel reactions by age, sex, and source of exposure. RESULTS: Piercing was significantly associated with female sex, being older than 18 years, and atopy (P < 0.003). Nickel sensitivity was associated with 1 or more piercing for men and women combined (P < 0.0001; relative risk [RR], 2.54; 95% confidence interval [CI], 2.35-2.75), although this association was stronger for men (RR, 2.21; 95% CI, 1.77-2.76) than women (RR, 1.47; 95% CI, 1.31-1.65). The frequency of positivity to nickel increased with number of piercings (14.3% for 1 piercing to 34.0% with ≥5 piercings). Piercing was not significantly associated with cobalt sensitivity alone (P = 0.8992) and was negatively associated with chromium sensitivity (P < 0.0001). Jewelry was the most common source of allergic reactions to nickel irrespective of sex or pierced status. CONCLUSIONS: Nickel sensitivity was significantly associated with piercing in both men and women. Jewelry was the most common source.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".