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Record W2410968372 · doi:10.2310/6620.2011.11051

Prevalence of Contact Allergy at a Dermatology Clinic in China from 1990-2009

2011· article· en· W2410968372 on OpenAlexvenueno aff
Xia Dou, Yi Zhao, Chunya Ni, Xuejun Zhu, Lingling Liu

Bibliographic record

VenueDermatitis · 2011
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineContact dermatitisAllergyDermatologySensitizationContact allergyPotassium dichromateAllergic contact dermatitisAllergenp-PhenylenediamineGeneral hospitalPediatricsImmunology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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.

Opus teacher head0.020
GPT teacher head0.255
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2011
Admission routes1
Has abstractyes

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