Topical Vitamin C in the Prevention and Treatment of Actinic Keratoses and Nonmelanoma Skin Cancers: A Systematic Scoping Review
Bibliographic record
Abstract
BACKGROUND: Vitamin C is an antioxidant that is involved in collagen synthesis, maintenance of the skin barrier, and neutralization of free oxygen radicals in the skin. The potential for use of topical vitamin C as a therapeutic and protective agent against actinic keratoses (AKs) and skin cancers has not been fully delineated. OBJECTIVE: To review the current evidence on topical vitamin C in the prevention and treatment of AKs and skin cancers. METHODS: A systematic search of the PubMed, MEDLINE, Embase, and Cochrane Clinical Trials was completed, and a scoping review was conducted. RESULTS: Four studies met inclusion criteria. Forty patients with AKs, 21 patients with basal cell carcinomas (BCCs), and 1 patient with a well-differentiated squamous cell carcinoma (SCC) underwent treatment with topical vitamin C. Two randomized controlled trials (RCTs), 1 prospective observational study, and 1 case report were included in this review. CONCLUSION: This scoping review found that highly concentrated topical formulations of vitamin C may have some benefit in the treatment of AKs and low-risk subtypes of nonmelanoma skin cancers. However, only a small number of clinical studies have been conducted on this topic, limiting the ability to generate clinical recommendations on this therapy.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".