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Record W4408326651 · doi:10.1097/jw9.0000000000000199

Systematic Review of Pediatric Photoprotection in Children of Color

2025· article· en· W4408326651 on OpenAlexaff
William Fitzmaurice, Aliyah King, Alexandra Firek, Fatma Zeynep Deligonul

Bibliographic record

VenueInternational Journal of Women’s Dermatology · 2025
Typearticle
Languageen
FieldMedicine
TopicSkin Protection and Aging
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPhotoprotectionMedicineDermatologySkin cancerSun exposureEnvironmental healthCancerBiology

Abstract

fetched live from OpenAlex

Background: Early childhood sun exposure contributes to lifetime risk of skin cancer. Many individuals from diverse ethnic backgrounds believe their skin tone confers immunity to sun damage; however, evidence of negative outcomes exists. Best practice in photoprotection for children of color is unclear. Objective: We sought to address the risks, benefits, and needs for sun protection and education in children of color. Methods: An English-language systematic literature review was conducted. Inclusion criteria were data on children of color and content relevant to photodamage and photoprotection. Results: Photoprotection is needed for children of all skin tones with photosensitivity. Individuals with darker skin tones have more innate photoprotection compared with lighter skin tone individuals, but both have incomplete photoprotection. Risk of nevus formation, skin cancer, and dyspigmentation are universal with varying degrees. Hispanic and Black communities are less likely to practice sun protection. Studies demonstrate need for early, culturally appropriate education about sun exposure in all communities. Limitations: Studies reviewed were of varied design and populations. Conclusion: This review determined that photoprotection has benefits for all patients, and the benefits of photoprotection should be taught early to children, caregivers, and parents of all skin tones using culturally appropriate approaches.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.005
GPT teacher head0.284
Teacher spread0.280 · 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 designSystematic review
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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