Cosmetic Versus Medicine: How Does Your Country Define Sunscreen?
Bibliographic record
Abstract
Skin cancer (melanoma and non-melanoma) is the most commonly diagnosed cancer in the United States of America, and non-melanoma skin cancer is the most common cause of Australian hospitalisations with cancer as the principle diagnosis, having a huge cost to the country's health care system. Primary and secondary skin cancer prevention is globally inadequate, with only 3 in 10 American adults using sun protection routinely. Evidence suggests that regular sunscreen use in Australians prevents both melanoma and non-melanoma skin cancers, and American research has found that daily sunscreen use reduced the incidence of melanoma - the most skin cancer deaths - by half. Despite this, in many countries and regions around the world, a major ongoing divergence remains on the classification of sunscreen as either a cosmetic product or a form of medical therapy, which in turn affects the consumers' attitudes towards the use of sunscreen. This is also affected by the increasing use of the internet, which has made the purchasing of products internationally convenient and easy for consumers worldwide, including sunscreen products, which are frequently marketed online. There is variation between each country or region and their regulations of sunscreen affect the consequent labelling claims of sunscreen products. This affects the unsuspecting consumer's choices in purchasing sun protection, which may be misinformed. Australia, Canada, and the US are the only countries to classify sunscreen as a form of medical therapy. This paper explores the current classification of sunscreen products in countries and regions around the world and discusses the impact of these discrepancies and similarities on the attitudes of consumers towards sunscreen use. Finally, we make suggestions on changes that can be made to encourage sunscreen use and safe sunscreen purchasing. J Drugs Dermatol. 2018;17(8):899-904.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".