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Record W2392414344 · doi:10.1097/der.0000000000000179

Chromate Allergy in Northern Israel in Relation to Exposure to Cement and Detergents

2016· article· en· W2392414344 on OpenAlexvenueno aff
Khalaf Kridin, Mogher Khamaisi, Sara Weltfriend

Bibliographic record

VenueDermatitis · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicChromium effects and bioremediation
Canadian institutionsnot available
Fundersnot available
KeywordsChromate conversion coatingMedicineHexavalent chromiumAllergyPopulationCementChromiumDermatologyEnvironmental healthImmunologyMetallurgy

Abstract

fetched live from OpenAlex

BACKGROUND: The prevalence of chromate allergy has declined worldwide in the last decades. OBJECTIVES: The aim of the study was to assess tendencies in chromate allergy in northern Israel and its possible causes. METHODS: Retrospective analysis of patch test data during 1999-2013 and a review of the medical records of patients with chromate allergy were conducted. RESULTS: A total of 4846 consecutive patients were patch tested, of whom 146 (3%) were found to be chromate sensitive. The prevalence of chromate allergy decreased significantly from 4.7% in 1999-2001 to 2.8% in 2002-2004 (P = 0.02). Since then, no significant fluctuations have occurred. A gradual and consistent decline in chromate allergy was recorded among women from 4.8% in 1999-2001 to 2.3% in 2008-2010. Cement (18.4%) was the most frequent source of exposure and was mainly observed in men. The frequency of clinically relevant cement exposure increased significantly from 7.7% in 2002-2004 to 28.7% in 2011-2013 (P = 0.04), whereas the frequency of relevant detergent exposure decreased significantly from 25% in 1999-2001 to 5.7% in 2011-2013 (P = 0.04). Hand (68.5%) was the most frequently involved anatomical site. CONCLUSIONS: The prevalence of chromate allergy in northern Israel is stable in the general population and gradually decreasing among women. These changes may be caused by reduced exposure to water-soluble hexavalent chromium in detergents but not in cement.

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 score0.068
Threshold uncertainty score0.969

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 head0.005
GPT teacher head0.197
Teacher spread0.191 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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