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Record W2014263546 · doi:10.1097/der.0b013e3182a5d8bc

Allergy to Cosmetics

2013· review· en· W2014263546 on OpenAlexvenueno aff
Jennifer I. Alani, Mark D.P. Davis, James A. Yiannias

Bibliographic record

VenueDermatitis · 2013
Typereview
Languageen
FieldMedicine
TopicContact Dermatitis and Allergies
Canadian institutionsnot available
Fundersnot available
KeywordsCosmeticsMedicineDermatologyPatch testingAllergic contact dermatitisPersonal careContact dermatitisContact allergySkin careAllergyIntensive care medicineFamily medicineNursingPathologyImmunology

Abstract

fetched live from OpenAlex

The term cosmetic has a broad definition and includes personal care products, hair care products, nail care products, and sunscreens. Modern cosmetics are safe for most users, and adverse reactions are very rare because the manufacturers invest heavily in safety, quality control, and product testing before releasing the product to the market. Despite these efforts, adverse reactions occur. Skin care products are major contributors to cosmetic allergic contact dermatitis (ACD), followed by hair care and nail care products. The most common allergens are fragrances and preservatives. The diagnosis of cosmetic allergy is established by reviewing the patient's clinical history and physical examination findings and confirmed with skin patch testing. Patch testing is the standard method for detecting allergens responsible for eliciting ACD. The purpose of this article was to review the prevalence, legislative laws, and role of patch testing in ACD.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.004

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 head0.045
GPT teacher head0.320
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations71
Published2013
Admission routes1
Has abstractyes

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