Bibliographic record
Abstract
INTRODUCTION: Onychomycosis is a fungal infection of the nail apparatus that affects 10 - 30% of the global population. Current therapeutic options for onychomycosis have a low to moderate efficacy and result in a 20 - 25% rate of relapse and reinfection. New therapeutic options are needed to broaden the spectrum of treatment options and improve the efficacy of treatment. AREAS COVERED: This review discusses the emerging pharmacotherapeutics; including topical reformulations of terbinafine, new azole molecules for systemic and topical administration, topical benzoxaboroles and topical polymer barriers. The paper also discusses device-based options, which may be designed to activate a drug or to improve drug delivery, such as photodynamic therapy and iontophoresis; laser device systems have also begun to receive regulatory approval for onychomycosis. EXPERT OPINION: Device-based therapeutic options for onychomycosis are expanding more rapidly than pharmacotherapy. Systemic azoles are the only class of pharmacotherapy that has shown a comparable efficacy to systemic terbinafine; however terbinafine remains the gold standard. The most notable new topical drugs are tavaborole, efinaconazole and luliconazole, which belong to the benzoxaborole and azole classes of drugs. Photodynamic therapy, iontophoresis and laser therapy have shown positive initial results, but randomized controlled trials are necessary to determine the long-term success of these devices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".