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Record W2808820286 · doi:10.1097/der.0000000000000393

Patch Testing to Propylene Glycol: The Mayo Clinic Experience

2018· article· en· W2808820286 on OpenAlexvenueno aff
Soogan C. Lalla, Henry Nguyen, Hafsa Chaudhry, Jill M. Killian, Lisa A. Drage, Mark D.P. Davis, James A. Yiannias, Matthew R. Hall

Bibliographic record

VenueDermatitis · 2018
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePatch testPatch testingAllergic contact dermatitisContact dermatitisSkin reactionDermatologyConcomitantPolyvinyl alcoholAllergenIrritant contact dermatitisCosmeticsAllergySurgeryOrganic chemistryImmunologyChemistryPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Propylene glycol (PG) is a solvent, vehicle, and humectant being used increasingly in a wide array of personal care products, cosmetics, and topical medicaments. Propylene glycol is a recognized source of both allergic and irritant contact dermatitis. OBJECTIVE: The aim of the study was to report incidence of positive patch tests to PG at Mayo Clinic. METHODS: We retrospectively reviewed records of all patients patch tested to PG from January 1997 to December 2016. RESULTS: A total of 11,738 patients underwent patch testing to 5%, 10%, or 20% PG. Of these, 100 (0.85%) tested positive and 41 (0.35%) had irritant reactions. Patients also tested to a mean of 5.6 concomitant positive allergens. The positive reaction rates were 0%, 0.26%, and 1.86% for 5%, 10%, and 20% PG, respectively, increasing with each concentration increase. The irritant reaction rates were 0.95%, 0.24%, and 0.5% for 5%, 10%, and 20% PG, respectively. CONCLUSIONS: Propylene glycol is common in skin care products and is associated with both allergic and irritant patch test reactions. Increased concentrations were associated with increased reactions.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score1.000

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)0.0010.001

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 head0.043
GPT teacher head0.313
Teacher spread0.271 · 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; both teacher heads agree on what is shown here.

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

Citations20
Published2018
Admission routes1
Has abstractyes

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