Systematic Review of Pediatric Photoprotection in Children of Color
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".