Photosensitizing Medications and Skin Cancer: A Comprehensive Review
Bibliographic record
Abstract
(1) The incidence of skin cancer is increasing in the United States (US) despite scientific advances in our understanding of skin cancer risk factors and treatments. In vitro and in vivo studies have provided evidence that suggests that certain photosensitizing medications (PSMs) increase skin cancer risk. This review summarizes current epidemiological evidence on the association between common PSMs and skin cancer. (2) A comprehensive literature search was conducted to identify meta-analyses, observational studies and clinical trials that report on skin cancer events in PSM users. The associated risks of keratinocyte carcinoma (squamous cell carcinoma and basal cell carcinoma) and melanoma are summarized, for each PSM. (3) There are extensive reports on antihypertensives and statins relative to other PSMs, with positive and null findings, respectively. Fewer studies have explored amiodarone, metformin, antimicrobials and vemurafenib. No studies report on the individual skin cancer risks in glyburide, naproxen, piroxicam, chlorpromazine, thioridazine and nalidixic acid users. (4) The research gaps in understanding the relationship between PSMs and skin cancer outlined in this review should be prioritized because the US population is aging. Thus the number of patients prescribed PSMs is likely to continue to rise.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.006 | 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".