Effect of Nicotinamide in Skin Cancer and Actinic Keratoses Chemoprophylaxis, and Adverse Effects Related to Nicotinamide: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background Oral nicotinamide is recommended in individuals with a field of cancerization or with ≥1 previous cutaneous squamous cell carcinoma (cSCC). Objective To evaluate the effect of nicotinamide in prevention of skin cancers. Methods We conducted a systematic review and meta-analysis of randomized controlled trials to evaluate the effect of nicotinamide. We used Medline, EMBASE, CENTRAL, and Web of Science databases from their inception to October 2020 to search the following concepts: “nicotinamide”; “randomized controlled trial” (validated filters). Two independent reviewers screened titles and abstracts for intervention and study design before searching full texts for eligibility criteria. To be eligible, ≥1 outcome had to be covered. We used a standardized collection grid to complete data extraction in duplicate. The primary outcome was skin cancers (all types). Secondary outcomes were basal cell carcinomas (BCCs); cSCCs; actinic keratoses; melanomas; digestive, cutaneous, and biochemical adverse effects (AEs). Subgroup analyses were planned a priori. Results We screened 4730 citations and found 29 trials (3039 patients) meeting inclusion criteria. Nicotinamide was associated with a significant reduction in skin cancers compared to control (rate ratio 0.50 (95% CI, 0.29-0.85; I 2 = 64%; 552 patients; 5 trials); moderate strength of the evidence). Heterogeneity was explained by risk of bias. Nicotinamide was associated with a significant reduction in BCCs and cSCCs, and increased risk of digestive AEs. Conclusion Oral nicotinamide should be considered in healthy patients or organ transplant recipients with history of skin cancer (GRADE: weak recommendation; moderate-quality evidence), in particular of BCC and cSCC.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.021 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".