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Record W4200436475 · doi:10.1097/der.0000000000000812

Prevalence of Contact Allergy to Nickel

2021· article· en· W4200436475 on OpenAlexaffvenueabout
Ilya Mukovozov, Nadia Kashetsky, Gillian de Gannes

Bibliographic record

VenueDermatitis · 2021
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsSt. Paul's HospitalSKiN HealthQuest University CanadaMemorial University of NewfoundlandUniversity of British Columbia
Fundersnot available
KeywordsMedicineContact dermatitisNickel allergyNickelPatch testRetrospective cohort studyAllergic contact dermatitisDermatologyNickel compoundsAllergyInternal medicineImmunologyMetallurgy

Abstract

fetched live from OpenAlex

BACKGROUND: No recent studies reporting nickel sensitivity prevalence in Canadians exist. OBJECTIVES: The aim of this study was to quantify nickel sensitivity prevalence in patients at a patch test clinic in Vancouver. METHODS: Retrospective chart review of 3263 patients patch tested for nickel sensitivity at our clinic in Vancouver between 2008 and 2020. RESULTS: In total, 24.3% (n = 792 of 3263) of patients were sensitive to nickel. Nickel sensitivity significantly increased over time from 24.3% to 27.9% from 2008 to 2020. Nickel-sensitive patients were significantly more likely to be women (P < 0.001), between the ages of 19 and 64 years (P = 0.010), and have dermatitis affecting the face (P = 0.001) and hands (P = 0.001). Nickel-sensitive patients were significantly less likely to be 65 years or older (P = 0.001) and have dermatitis affecting the legs (P = 0.002). Approximately half of nickel-sensitive reactions were new positive reactions at the second reading. CONCLUSIONS: Nickel sensitivity occurred in approximately one quarter of patients and significantly increased over time. Nickel-sensitive patients were more likely to be women, aged 19 to 64 years, and have dermatitis affecting the face and hands; and less likely to be 65 years or older and have dermatitis affecting the legs.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.651
Threshold uncertainty score0.999

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)0.0020.000

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 head0.257
Teacher spread0.243 · 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

Citations9
Published2021
Admission routes3
Has abstractyes

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