An Epidemiological Update on Indoor Tanning and the Risk of Skin Cancers
Bibliographic record
Abstract
Indoor tanning (sunbeds, solarium) uses artificial ultraviolet radiation (UVR) to stimulate cosmetic tanning of the skin. Indoor tanning has been officially classified as a human carcinogen in 2009 by the International Agency for Research on Cancer of the World Health Organization (WHO). The differences in the prevalence of sunbed use across countries and over the years highlight underlying legislative, climatic, and cultural differences. Indoor tanning-seeking behaviors may be driven by motivations for an appealing appearance, largely influenced by gender and age, and several misconceptions that a prevacation tan safeguards the skin, that sunbeds can be used to treat acne or to increase vitamin D, or that tanning is a healthy habit. This review provides an epidemiological update on the prevalence of sunbed use, who tends to use sunbeds and why, and details the current evidence on the association of sunbeds with skin cancers, including cutaneous melanoma, basal cell carcinoma (BCC), and cutaneous squamous cell carcinoma (cSCC). A statistically significant higher risk of cutaneous melanoma, BCC and cSCC with the use of sunbeds has been consistently demonstrated. This risk of skin cancer is even higher with the more frequent use of sunbeds, underscoring a dose-response relationship, and in those first exposed to sunbeds at a younger age. Preventive measures against sunbed use include legislation restricting sunbed use, educational campaigns to inform and discourage from indoor tanning, as well as using the internet, online advertising messages and the social media to reach larger audiences and to promote an untanned appearance.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".