A systematic review of the risk of cutaneous malignancy associated with ultraviolet nail lamps: what is the price of beauty?
Bibliographic record
Abstract
Gel manicures have become part of a popular personal care service in the last two decades due to increased longevity of the polish and the added strength to the nail plate. Prolonged exposure to nail ultraviolet (UV) lamps is required to cure the gel polish. Despite the increased use of UV nail lamps, there is limited consensus in the literature on the risk of skin malignancy associated with UV nail lamps. The objective of this article was to provide a systematic review of the risk of skin malignancy associated with the use of UV nail lamps and to synthesize evidence-based recommendations on their safe usage. A systematic review of the literature was conducted on the databases, Medline and Embase, in accordance with PRISMA guidelines. The search yielded 2,331 non-duplicate articles. Nine were ultimately included, of which three were case reports, one was a cross-sectional study, and five were experimental studies. The risk of bias per the Joanna Briggs Institute guidelines was high or unclear, likely due to the number of case reports included. Prolonged and repeated exposure to UV nail lamps may pose a low risk of skin cancer. It is important to note that the available evidence is weak, and patients should be informed about the limited data to make their own decisions. Dermatologists and other healthcare providers should be updated with the latest evidence to address patients' concerns about gel manicures and suggest practices which can effectively reduce the risk of cutaneous malignancy associated with gel manicures, such as the use of UV-blocking gloves or properly applied sunscreens.
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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 | 0.011 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".