Associations of Nickel Co-Reactions and Metal Polysensitization in Adults
Bibliographic record
Abstract
BACKGROUND: Allergic contact dermatitis to metals is a significant clinical and public health problem. Little is known about the determinants of polysensitization to metals. OBJECTIVE: The aim of the study was to determine the frequency and predictors of nickel co-reactions and metal polysensitization. METHODS: This is a retrospective chart review of 686 adults (age ≥ 18 years) who were patch tested from 2014 to 2017. RESULTS: Overall, 267 patients (38.9%) had 1 or more positive patch-test reactions to a metal allergen, most commonly nickel (17.4%), mercury (12.3%), and palladium (9.2%). Nickel reactions were inversely associated with age (logistic regression; adjusted odds ratio [95% confidence interval], 0.39 [0.29-0.78]). Among patients with positive reactions to nickel, 34.5%, 15.1%, and 5.0% had positive reactions to 1, 2, or 3 additional metals, respectively. The most common nickel co-reactors were palladium, mercury, and gold. Polysensitization to metals occurred in 11.8% of patients. Polysensitization to metal allergens was associated with female sex (6.67 [1.01-44.21]) and inversely associated with age (0.40 [0.18-0.88]). CONCLUSIONS: Nickel-sensitized patients have high rates of metal co-reactions. Polysensitization to metals is common in adults. These results may help guide future strategies for allergen avoidance.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".