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Record W2321149462 · doi:10.1097/der.0b013e3182937aa4

Diagnosis and Prevalence of Lanolin Allergy

2013· article· en· W2321149462 on OpenAlexvenueno aff
Rachel Y. Miest, James A. Yiannias, Yu‐Hui Chang, Nidhi Singh

Bibliographic record

VenueDermatitis · 2013
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsnot available
Fundersnot available
KeywordsLanolinMedicineAllergyDermatologyImmunologyChromatography

Abstract

fetched live from OpenAlex

BACKGROUND: Current evaluation of suspected allergic contact dermatitis to lanolin includes patch testing to lanolin alcohol (30% in petrolatum). Using this method, the prevalence of lanolin allergy is low (1.8%-2.5%). OBJECTIVE: The objective of this study was to determine whether patch testing to a single lanolin derivative results in underdiagnosis compared with patch testing to 12 lanolin derivatives. METHODS: Patients were prospectively patch tested to (1) lanolin alcohol (30% in petrolatum) in our standard allergen series; (2) Amerchol L101 (50% in petrolatum) in our cosmetic series; and (3) 10 lanolin derivatives (using concentrations and vehicles recommended in the literature) in a supplemental series. RESULTS: Of 286 patients, the overall prevalence of positive reactions to lanolin in at least 1 of the 3 patch test series was 6.29% (95% confidence interval [CI], 3.48%-9.11%) (n = 18). The prevalence rates of lanolin allergy using the standard, cosmetic, and supplemental series were 1.05% (95% CI, 0%-2.23%), 3.85% (95% CI, 1.62%-6.07%), and 3.85% (95% CI, 1.62%-6.07%), respectively. Amerchol L101 was associated with increased reaction rates compared with the standard (odds ratio, 3.81; P = 0.007) and supplemental (odds ratio, 8.85; P < 0.001) series, whereas reaction rates were similar for the standard and supplemental series (P = 0.78). CONCLUSIONS: Amerchol L101 and patients' own products should be added to a standard patch testing allergen series to adequately identify lanolin 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score0.999

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)0.0020.000

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 head0.012
GPT teacher head0.236
Teacher spread0.224 · 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.

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

Citations31
Published2013
Admission routes1
Has abstractyes

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