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Record W2901741937 · doi:10.1097/der.0000000000000421

Associations of Nickel Co-Reactions and Metal Polysensitization in Adults

2018· article· en· W2901741937 on OpenAlexvenueno aff
Supriya Rastogi, Kevin R. Patel, Vivek Singam, Harrison H. Lee, Jonathan I. Silverberg

Bibliographic record

VenueDermatitis · 2018
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNickelOdds ratioContact dermatitisLogistic regressionConfidence intervalAllergenAllergic contact dermatitisMercury (programming language)Skin reactionMetalPatch testNickel allergyDermatologyInternal medicineImmunologyAllergyMetallurgyMaterials science

Abstract

fetched live from OpenAlex

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.014
GPT teacher head0.275
Teacher spread0.261 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations17
Published2018
Admission routes1
Has abstractyes

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