Prevalence of Contact Allergy at a Dermatology Clinic in China from 1990-2009
Bibliographic record
Abstract
BACKGROUND: The prevalence of contact allergy varies in different regions and populations. OBJECTIVE: To describe the frequency of sensitization in patients with dermatitis or eczema referred to Peking University First Hospital and analyze the trends in the prevalence of common allergens from January 1, 1990, to December 31, 2009. METHODS: A total of 1,858 patients were patch tested with the Chinese baseline series of contact allergens. Data were collected from retrospective charts and analyzed. RESULTS: Positive reactions to one or more allergens were shown in 1,374 patients (74.0%). The most common sensitizers were nickel sulfate (25.7%), fragrance mix I (25.6%), thiuram mix (25.5%), ammoniated mercury (20.5%), and p-phenylenediamine (19.1%). A statistically significant increase of sensitization over the 20-year period was seen for nickel sulfate, fragrance mix, ammoniated mercury, colophony, ethylenediamine, and potassium dichromate. Mercapto mix showed a trend of a statistically significant decrease in sensitizations from 1990 to 2009. CONCLUSIONS: The patterns of contact allergy in patients from Peking University Hospital are different from those of patients in other regions of China, in European countries, and in the United States. Nickel and fragrance mix were the most common allergens, and the sensitization rates of these two allergens had been increasing remarkably during the 20 years from 1990 to 2009.
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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 | 0.001 |
| 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) | 0.002 | 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".