How Effective Are Devices in the Management of Onychomycosis?: A Systematic Review
Bibliographic record
Abstract
Onychomycosis is the most common nail disorder, with a global prevalence of approximately 5.5%. It is difficult to cure on both short-term and long-term bases. The most common treatments include the use of oral or topical antifungals. Recurrent infections are common, and the use of systemic oral antifungals raises concerns of hepatotoxicity and drug-drug interactions, particularly in patients with polypharmacy. A number of device-based treatments have been developed for onychomycosis treatment, to either directly treat fungal infection or act as adjuvants to increase the efficacy of topical and oral agents. These device-based treatments have been increasing in popularity over the past several years, and include photodynamic therapy, iontophoresis, plasma, microwaves, ultrasound, nail drilling, and lasers. Some, such as photodynamic therapy, provide more direct treatment, whereas others, such as ultrasound and nail drilling, aid the uptake of traditional antifungals. We conducted a systematic literature search investigating the efficacy of these device-based treatment methods. From an initial result of 841 studies, 26 were deemed relevant to the use of device-based treatments of onychomycosis. This review examines these methods and provides insight into the state of clinical research for each. Many device-based treatments show promising results, but require more research to assess their true impact on onychomycosis.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| 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".