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Record W3034980671 · doi:10.1097/dss.0000000000002478

Photoprotection With Mineral-Based Sunscreens

2020· review· en· W3034980671 on OpenAlexaff
Nowell Solish, Shannon Humphrey, Brittany Waller, Sherri Vanderveen

Bibliographic record

VenueDermatologic Surgery · 2020
Typereview
Languageen
FieldMedicine
TopicSkin Protection and Aging
Canadian institutionsUniversity of British ColumbiaUniversity of Toronto
Fundersnot available
KeywordsPhotoprotectionSun protectionSun protection factorUltraviolet radiationMedicineBroad spectrumInternet privacyDermatologyComputer scienceChemistry

Abstract

fetched live from OpenAlex

BACKGROUND: Although chemical sunscreens have traditionally been at the forefront of sun protection, safety concerns and increasing awareness of the environmental impact of personal-care products have led to greater interest in the use of mineral blockers as photoprotective agents. OBJECTIVE: To examine the safety and efficacy of mineral-based sunscreens to allow patients to make informed choices about ultraviolet (UV) protection. MATERIALS AND METHODS: A review of the literature was performed using the PubMed database. RESULTS: This article provides an overview of physical blockers and focuses on the efficacy of mineral sunscreens in offering broad-spectrum UV protection and safety concerns, including the controversy surrounding the use of nanoparticles. Practical tips for application are also reviewed. CONCLUSION: Mineral sunscreens are an attractive, efficacious option for consumers who prefer alternative choices in sun protection.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.095
GPT teacher head0.318
Teacher spread0.223 · 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 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

Citations7
Published2020
Admission routes1
Has abstractyes

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