A comprehensive review of nondermatophyte mould onychomycosis: Epidemiology, diagnosis and management
Bibliographic record
Abstract
Nondermatophyte moulds (NDMs) are widely distributed and can be detected in association with mycotic nails; however, sometimes it can be challenging to establish the role of NDMs in the pathogenesis of onychomycosis (i.e. causative vs. contaminant). In studies where the ongoing invasive presence of NDMs is confirmed through repeat cultures, the global prevalence of NDMs in onychomycosis patients is estimated at 6.9% with the 3 most common genus being: Aspergillus, Scopulariopsis and Fusarium. NDM onychomycosis can, in many cases, appear clinically indistinguishable from dermatophyte onychomycosis. Clinical features suggestive of NDMs include proximal subungual onychomycosis with paronychia associated with Aspergillus spp., Fusarium spp. and Scopulariopsis brevicaulis, as well as superficial white onychomycosis in a deep and diffused pattern associated with Aspergillus and Fusarium. Longitudinal streaks seen in patients with distal and lateral onychomycosis may serve as an additional indicator. For diagnosis, light microscopic examination should demonstrate fungal filaments consistent with an NDM with at least two independent isolations in the absence of a dermatophyte; the advent of molecular testing combined with histological assessment may serve as an alternative with improved sensitivity and turnover time. In most instances, antifungal susceptibility testing has limited value. Information on effective treatments for NDM onychomycosis is relatively scarce, unlike the situation in the study of dermatophyte onychomycosis. Terbinafine and itraconazole therapy (continuous and pulsed) appear effective to varying extents for treating onychomycosis caused by Aspergillus, Fusarium or Scopulariopsis. There is scant literature on oral treatments for Neoscytalidium.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".