Allergen Composition, Marketing Claims, and Affordability of Pediatric Sunscreens
Bibliographic record
Abstract
BACKGROUND: Childhood sun exposure is associated with development of future skin cancers. Sunscreens are an important tool to prevent harmful ultraviolet rays. OBJECTIVES: The aims of the study are to evaluate sunscreens targeted to children and to analyze cost, marketing claims, ingredients, and allergens to help consumers select products. METHODS: The top 50 pediatric sunscreens across retailers were analyzed for their cost, marketing claims, ingredients, vehicles, and containers. Ingredients were compared with the American Contact Dermatology Society 2020 Core Allergen List. RESULTS: The mean price was $6.20 per ounce (range, $0.25-$39.98). The mean sun protection factor was 48.5 (range, 30-100; SD, 48.5). There was a mean of 17.5 ingredients and a mean of 1.1 allergens in products. On average, products marketed as "sensitive skin" were not only significantly more expensive ($8.90 vs $3.50 per ounce, P = 0.01) but also were significantly more likely to not contain any allergens (36.0%, n = 18 vs 12%, n = 6; P = 0.05). Products with mineral-only UV blockers were significantly less likely to have any allergen when compared with products that had chemical UV blockers (5.6%, n = 1 vs 94.4%, n = 17; P = 0.02). CONCLUSIONS: The current market of pediatric sunscreens varies significantly in price, marketing claims, and active ingredients. Products marked as suitable for sensitive skin had significantly fewer allergens, but a majority of these products still had at least one allergen. Many sunscreens contain contact allergens, which is an important selection consideration.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".