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Record W4307181465 · doi:10.1097/der.0000000000000963

Sunscreens: A Review of UV Filters and Their Allergic Potential

2022· review· en· W4307181465 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueDermatitis · 2022
Typereview
Languageen
FieldMedicine
TopicSkin Protection and Aging
Canadian institutionsnot available
Fundersnot available
KeywordsUV filterAllergic contact dermatitisMedicineBenzophenoneDermatologyOrganic chemistryAllergyChemistryImmunology

Abstract

fetched live from OpenAlex

Active ingredients of sunscreens, or UV filters, have increased in use because public awareness of sun safety has risen. In addition to this intentional use, unintentional exposures to UV filters also occur through application of personal care products, where the filters are incorporated into the product. There are 2 main types of UV filters: organic (chemical) filters and the 2 inorganic (mineral) filters, titanium dioxide and zinc oxide. Both allergic contact dermatitis (ACD) and photoallergic contact dermatitis (PACD) have been caused by organic filters; oxybenzone (benzophenone-3) is the most frequently reported contact and photocontact allergen compared with all other UV filters. There are no reports of ACD or PACD to the inorganic (physical) UV filters. Here, we review the categories of sunscreens available, currently marketed UV filters, and their corresponding ACD and PACD.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.857
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0030.000

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 head0.044
GPT teacher head0.305
Teacher spread0.261 · 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