Bibliographic record
Abstract
PURPOSE OF REVIEW: Physicians need to be prepared to counsel patients on why and how to protect themselves from damaging ultraviolet (UV) radiation, including the proper use of sunscreens. In this article, we review the interplay between UV radiation, sunscreens and the skin, highlighting current controversies and recommendations surrounding sunscreen use. RECENT FINDINGS: An important concept is that excessive UV exposure has long-term damaging effects on the skin beyond the immediate sunburn. Recent discoveries of the role of UVA radiation in skin cancer development have set high standards for broad-spectrum coverage to be met by sunscreens. Current evidence does not support an association between sunscreen use and melanoma, systemic toxicity or vitamin D deficiency. Although sunscreen application is the most common modality for sun protection, many people do not use it correctly. Regular sunscreen use during childhood and adolescence can significantly reduce lifetime incidence of skin cancer; therefore, targeting children in pediatric offices regarding unprotected UV exposure may be a practical approach. SUMMARY: Sunscreens continue to be a major method of photoprotection among the public, offering numerous benefits that clearly outweigh potential risks; however, optimizing the use of sunscreens, especially among children and adolescents, remains a major challenge.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".