Energy-Based Devices for the Treatment of Cutaneous Verrucae: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Warts are one of the most common benign neoplasms caused by human papillomavirus infection and often pose a therapeutic challenge. OBJECTIVE: To summarize the current evidence on the safety and efficacy of laser and energy-based devices for the treatment of cutaneous verrucae. METHODS: A comprehensive systematic review of the literature on laser and energy-based devices for the treatment of cutaneous verrucae was performed. RESULTS: A total of 904 unique studies were identified, of which 109 were included in this review. The most commonly used lasers as a single treatment modality for verrucae included the long-pulsed Nd:Yag (n = 20) and pulsed dye (n = 18) lasers. Other modalities included the CO2 ablative laser (n = 10), photodynamic therapy (n = 11), local hyperthermia (n = 11), microwave therapy (n = 2), and nanopulse stimulation (n = 1). Other studies combined energy-based modalities with additional treatments, such as retinoids, imiquimod, and intralesional bleomycin. Overall, such devices were generally well-tolerated, with only a mild side effect profile. CONCLUSION: Overall, the use of laser and energy-based devices is a safe and well-tolerated option for cutaneous verrucae that is relatively less invasive than surgical interventions. Future studies using more consistent outcome assessment tools will be valuable to help clinicians develop device-specific protocols and treatment regimens to ensure replicable and effective outcomes.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.006 | 0.007 |
| 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) | 0.005 | 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".