MétaCan
Menu
Back to cohort
Record W2288016144 · doi:10.2310/6620.2009.09037

Questionnaire Study of the Prevalence of Allergic Contact Dermatitis from Cosmetics in Israel

2009· article· en· W2288016144 on OpenAlexvenueno aff
Akiva Trattner, Dan Slodownik, Adnan Jbarah, Arieh Ingber

Bibliographic record

VenueDermatitis · 2009
Typearticle
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsnot available
Fundersnot available
KeywordsCosmeticsMedicineDermatologyAllergic contact dermatitisPatch testRashAllergyContact dermatitisContact allergyPopulationAllergenEnvironmental healthImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: The prevalence of cosmetics-related contact dermatitis is rising, owing mainly to the wider use of cosmetics by the public and the routine diagnostic use of patch tests. OBJECTIVE: To determine the prevalence of cosmetics allergy in Israel. METHODS: A questionnaire was distributed to a random sample of 360 female customers of pharmacies and beauty salons in two areas of the country. Items included general health profile, family history of atopy, occurrence of rash due to patch-test-proven cosmetics allergy, anatomic sites of the rash, subjective aspects regarding the rash, and cosmetics consumption habits. RESULTS: Patient age ranged from 15 to 89 years. Eleven subjects (3.1%) had patch-test-proven cosmetics allergic contact dermatitis. There was a correlation between proven cosmetics allergy and subjective sensitivity to facial cream (p = .03). CONCLUSIONS: The 3.1% prevalence rate of cosmetics contact allergic dermatitis in a randomly selected population in Israel is similar to values reported in the literature (about 2%). The higher-than-expected rate of subjective sensitivity to facial cream among patients with proven cosmetics allergy may be explained by the wide use of facial cream, facial skin susceptibility to insult, and the relatively long duration of contact of facial cream (a leave-on product) with the skin.

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 score0.024
Threshold uncertainty score0.557

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 head0.012
GPT teacher head0.256
Teacher spread0.244 · 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

Citations12
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueDermatitisSame topicContact Dermatitis and AllergiesFrench-language works237,207