Safety and efficacy of new generation azole antifungals in the management of recalcitrant superficial fungal infections and onychomycosis
Bibliographic record
Abstract
INTRODUCTION: Terbinafine is considered the gold standard for treating skin fungal infections and onychomycosis. However, recent reports suggest that dermatophytes are developing resistance to terbinafine and the other traditional antifungal agents, itraconazole and fluconazole. When there is resistance to terbinafine, itraconazole or fluconazole, or when these agents cannot used, for example, due to potential drug interactions with the patient's current medications, clinicians may need to consider off-label use of new generation azoles, such as voriconazole, posaconazole, fosravuconazole, or oteseconazole. It is essential to emphasize that we do not advocate the use of newer generation azoles unless traditional agents such as terbinafine, itraconazole, or fluconazole have been thoroughly evaluated as first-line therapies. AREAS COVERED: This article reviews the clinical evidence, safety, dosage regimens, pharmacokinetics, and management algorithm of new-generation azole antifungals. EXPERT OPINION: Antifungal stewardship should be the top priority when prescribing new-generation azoles. First-line antifungal therapy is terbinafine and itraconazole. Fluconazole is a consideration but is generally less effective and its use may be off-label in many countries. For difficult-to-treat skin fungal infections and onychomycosis, that have failed terbinafine, itraconazole and fluconazole, we propose consideration of off-label voriconazole or posaconazole.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".