Utility of devices for onychomycosis: a review
Bibliographic record
Abstract
Onychomycosis is difficult to treat due to long treatment durations, poor efficacy rates of treatments, high relapse rates, and safety issues when using systemic antifungal agents.Device-based treatments are targeted to specific regions of the nail, have favorable safely profiles, and do not interfere with systemic agents.They may be an effective alternative therapy for onychomycosis especially with increasing reports of squalene epoxidase gene mutations and potential resistance to terbinafine therapy.In this review, we discuss four devices used as antifungal treatments and three devices used as penetration enhancers for topical agents.Lasers, photodynamic therapy, microwaves, and non-thermal plasma have the capacity to inactivate fungal pathogens demonstrated through in vivo studies.Efficacy rates for these devices, however, remain relatively low pointing toward the need to further optimize device or usage parameters.Ultrasound, nail drilling, and iontophoresis aid in improving the permeability of topical agents through the nail and have been investigated as adjunctive therapies.Due to the paucity in clinical data, their efficacy in treating onychomycosis has not yet been established.While the results of clinical studies point toward the potential utility of devices for onychomycosis, further large-scale randomized clinical trials following regulatory guidelines are required to confirm current results.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".