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Record W2766870088 · doi:10.1097/der.0000000000000327

Patch Testing with Decyl and Lauryl Glucoside: How Well Does One Screen for Contact Allergic Reactions to the Other?

2017· article· en· W2766870088 on OpenAlexvenueno aff
Rachel K. Severin, D. Belsito

Bibliographic record

VenueDermatitis · 2017
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsnot available
Fundersnot available
KeywordsGlucosideMedicineAllergic contact dermatitisPatch testingContact dermatitisDermatologyAlkylAllergenAllergyPatch testReactivity (psychology)Contact allergyOrganic chemistryChemistryImmunologyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Alkyl glucoside surfactants, present in many cosmetic products, can cause allergic contact dermatitis. Decyl glucoside has been part of the North American Contact Dermatitis Group standard allergen panel since 2009. OBJECTIVES: This study aimed to identify rates and relevance of positive patch test reactions to decyl and lauryl glucosides and to determine how well one of these glucosides screens for contact allergic reactions to the other. METHODS: A retrospective analysis was performed on 897 patients suspected of having a cosmetic-related dermatitis and patch tested with both decyl and lauryl glucosides between 2009 and 2016. RESULTS: Forty-eight patients (5%) had positive reactions to decyl glucoside and/or lauryl glucoside. Among the alkyl glucoside-allergic patients, 65% had positive reactions to both decyl and lauryl glucosides. In 41% of cases, reactions were of definite or probable relevance. In approximately 55% of cases, reactions were of possible relevance. CONCLUSIONS: Sixty-five percent of glucoside-allergic patients exhibited co-reactions to decyl and lauryl glucosides. Thus, neither glucoside is an adequate screen for allergy to the other. Given that these reactions are often relevant, clinicians should patch test with decyl, lauryl, and other alkyl glucosides in cases of suspected cosmetic allergy.

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 score0.222
Threshold uncertainty score0.675

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 studies0.0010.000
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 head0.033
GPT teacher head0.266
Teacher spread0.233 · 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

Citations23
Published2017
Admission routes1
Has abstractyes

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