Adverse Reactions to Sunscreen Agents: Epidemiology, Responsible Irritants and Allergens, Clinical Characteristics, and Management
Bibliographic record
Abstract
Sunscreen is a key component in the preventive measures recommended by dermatologists and public health campaigns aimed at reducing sunburn, early skin aging, and skin cancer. To maximize compliance, adverse reactions to sunscreens should be minimized. Although inactive ingredients cause many of these reactions, it is important for dermatologists to be aware of reactions to active ultraviolet filters. There are approximately 120 chemicals that can function as ultraviolet (UV) filters. This review focuses on the 36 most common filters in commercial and historical use. Of these, 16 are approved for use by the US Food and Drug Administration. The benzophenones and dibenzoylmethanes are the most commonly implicated UV filters causing allergic and photoallergic contact dermatitis (PACD) reactions; benzophenone-3 is the leading allergen and photoallergen within this class. When clinically indicated, patch and photopatch testing should be performed to common UV filters.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".