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Record W65362882 · doi:10.2310/6620.2010.10004

Disproportionated Rosin Dehydroabietic Acid in Neoprene Surgical Gloves

2010· article· en· W65362882 on OpenAlexvenueno aff
Paul D. Siegel, Brandon F. Law, Joseph F. Fowler, Lynn M. Fowler

Bibliographic record

VenueDermatitis · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiological Activity of Diterpenoids and Biflavonoids
Canadian institutionsnot available
Fundersnot available
KeywordsNeopreneRosinGloveboxMedicineAbietic acidDichloromethaneOrganic chemistryChemistryNatural rubberResin acid

Abstract

fetched live from OpenAlex

BACKGROUND: Allergic contact dermatitis (ACD) is a well-recognized immune-mediated disease often associated with the use of vulcanization accelerator-containing latex and nitrile gloves. Potential contact allergens in neoprene (polychloroisoprene, polychloroprene) gloves have not been reported. OBJECTIVE: The objective was to analyze extracts of neoprene surgical and examination gloves for potential contact allergens. METHODS: Four different brands of neoprene-type gloves were purchased, and dichloromethane extracts were derivatized and assayed by gas chromatographic mass spectrometry. A latex surgical glove was used as a negative control. RESULTS: Chemical species consistent with the composition of disproportionated rosin (dehydroabietic acid [DHA], didehydroabietic acid, and other pimaric or isopimaric species) were identified in dichloromethane extracts of neoprene gloves. Levels of DHA, a type IV prohapten that can be air oxidized to an active allergen, ranged from 7 to 31 mg/g of glove. A leaching study of DHA was conducted, and small amounts of DHA leached from the glove materials into artificial sweat. DHA oxidation products were not observed in any of the gloves assayed. CONCLUSION: DHA exposure may occur from neoprene-type glove use, although a potential association with glove ACD has not been established.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.444

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 head0.227
Teacher spread0.222 · 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 designBench or experimental
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

Citations6
Published2010
Admission routes1
Has abstractyes

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