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Record W4414451931 · doi:10.1177/17103568251382236

Allergenicity and Unregulated Marketing Claims in Tinted Sunscreens

2025· article· en· W4414451931 on OpenAlexvenueno aff
Lauren Gawey, Aditya Joshi, Nasir Rahman, Alexandra Gottsegen, Raveena Ghanshani, Khiem A. Tran, Jennifer L. Hsiao, Lisa E. Maier, Vivian Y. Shi

Bibliographic record

VenueDermatitis · 2025
Typearticle
Languageen
FieldMedicine
TopicSkin Protection and Aging
Canadian institutionsnot available
Fundersnot available
KeywordsFluid ounce (US)AllergenCosmeticsProduct (mathematics)PopularityAllergic contact dermatitis

Abstract

fetched live from OpenAlex

Abstract: Background: Tinted sunscreens are gaining popularity among skin of color populations due to their protection against visible light-induced hyperpigmentation and elimination of the white cast. Despite growing use, little is known about their allergenicity or the reliability of safety-oriented marketing claims. Objective: To assess the relationship between allergen content, active ingredients, marketing claims, and price in top-rated, best-selling tinted sunscreens. Methods: We conducted an analysis of 49 tinted sunscreens from 3 major online retailers. Products were evaluated for allergens listed in the 2020 American Contact Dermatitis Society Core Allergen Series, presence of marketing claims, UV filters, and price per ounce. Results: Ninety-eight percent of products contained at least 1 allergen (mean 3.1). The average price per ounce was $18.95. Products with multiple shade options were more expensive ($22.92 vs $16.65/oz; P = 0.04), while “nano-particle free” products were less expensive ($14.26 vs $20.83/oz; P = 0.04). Marketing claims were abundant (mean 11.7). “Fragrance free,” “cruelty free,” “reef friendly,” and “nano-particle free” claims were associated with fewer allergens ( P < 0.05). Octisalate and octinoxate correlated with more allergens; zinc oxide with fewer ( P < 0.05). Conclusions: Tinted sunscreens frequently contain allergens despite safety-related marketing claims. Clinicians should guide product selection based on allergenic potential, active ingredients, and cost.

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 score0.279
Threshold uncertainty score0.251

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 head0.008
GPT teacher head0.259
Teacher spread0.251 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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