The Use of Retinoids for the Prevention and Treatment of Skin Cancers: An Updated Review
Bibliographic record
Abstract
Retinoids are natural and synthetic vitamin A derivatives that are effective for the prevention and the treatment of non-melanoma skin cancers (NMSC). NMSCs constitute a heterogenous group of non-melanocyte-derived skin cancers that impose substantial burdens on patients and healthcare systems. They include entities such as basal cell carcinoma and cutaneous squamous cell carcinoma (collectively called keratinocyte carcinomas), cutaneous lymphomas and Kaposi's sarcoma among others. The retinoid signaling pathway plays influential roles in skin physiology and pathology. These compounds regulate diverse biological processes within the skin, including proliferation, differentiation, angiogenesis and immune regulation. Collectively, retinoids can suppress skin carcinogenesis. Both topical and systemic retinoids have been investigated in clinical trials as NMSC prophylactics and treatments. Desirable efficacy and tolerability in clinical trials have prompted health regulatory bodies to approve the use of retinoids for NMSC management. Acceptable off-label uses of these compounds as drugs for skin cancers are also described. This review is a comprehensive outline on the biochemistry of retinoids, their activities in the skin, their effects on cancer cells and their adoption in clinical practice.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".