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Record W2465450452 · doi:10.1097/der.0000000000000200

Leather Contains Cobalt and Poses a Risk of Allergic Contact Dermatitis: Cobalt Indicator Solution and X-ray Florescence Spectrometry as Screening Tests

2016· article· en· W2465450452 on OpenAlexvenueno aff
Dathan Hamann, Carsten R. Hamann, Patrick Kishi, Torkil Menné, Jeanne Duus Johansen, Jacob P. Thyssen

Bibliographic record

VenueDermatitis · 2016
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsnot available
Fundersnot available
KeywordsCobaltMedicineAllergic contact dermatitisContact dermatitisX-rayDermatologyMetallurgyAllergyMaterials scienceImmunologyOptics

Abstract

fetched live from OpenAlex

BACKGROUND: Cobalt was recently identified in a leather couch responsible for dermatitis. Cobalt content/release in leather in the United States is unknown. We evaluated leather for cobalt content/release and investigated screening methods for identifying cobalt in leather. METHODS: One hundred thirty-one leather swatches were screened for cobalt content/release with X-ray fluorescence (XRF) spectrometry and cobalt indicator solution (CIS). Samples with positive screens and 1 negative control were analyzed using inductively-coupled plasma mass spectrometry (ICPMS). RESULTS: CIS showed that 5 of 131 samples contained cobalt, subsequently found to be between 1 and 190 parts per million (ppm) when evaluated with ICPMS. The XRF analysis showed that 6 samples contained >5% cobalt, subsequently found to contain greater than 300 ppm cobalt by ICPMS. 7 of 12 tested swatches contained cobalt in excess of 100 ppm. One sample contained greater than 1000 ppm cobalt. The prevalence of swatches containing cobalt at levels in excess of 190 ppm was at least 5% (n = 7; total, N = 131). DISCUSSION: Some leather consumer goods contain and release cobalt. Cobalt indicator solution is a poor screening test for cobalt in leather while XRF screening may be effective. Leather is a new source of cobalt exposure. Exposures to metal allergens are changing in ways that impact clinical decision making.

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 score0.057
Threshold uncertainty score0.962

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)0.0000.000

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 head0.010
GPT teacher head0.244
Teacher spread0.234 · 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

Citations35
Published2016
Admission routes1
Has abstractyes

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