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Record W4414857870 · doi:10.1097/dss.0000000000004861

Topical Vitamin C in the Prevention and Treatment of Actinic Keratoses and Nonmelanoma Skin Cancers: A Systematic Scoping Review

2025· article· en· W4414857870 on OpenAlexaff
Leah Johnston, Santina Conte, Ilya Mukovozov, Jennifer Beecker, Susan Poelman

Bibliographic record

VenueDermatologic Surgery · 2025
Typearticle
Languageen
FieldNursing
TopicVitamin C and Antioxidants Research
Canadian institutionsWomen's College HospitalMcGill UniversityUniversity of CalgaryUniversity of Ottawa
Fundersnot available
KeywordsActinic keratosesLimitingVitamin CPhotodermatosisDyskeratosisClinical trialSkin cancerMEDLINE

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.393
Threshold uncertainty score0.247

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.357
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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