Epidemiology of Onychomycosis in the United States Characterized Using Molecular Methods, 2015–2024
Bibliographic record
Abstract
Onychomycosis is a recalcitrant fungal infection of the nail unit that can lead to secondary infections and foot complications. Accurate pathogen identification by confirmatory testing is recommended to improve treatment outcomes. In this study, we reviewed the records of 710,541 patients whose nail specimens were sent to a single molecular diagnostic laboratory between 2015 and 2024. PCR testing revealed a more comprehensive spectrum of pathogens than previously reported, which was corroborated by the demonstration of fungal invasion on histopathology. Consistent with our current understanding, the T. rubrum complex (54.3%) are among the most common pathogens; however, a significant portion of mycology-confirmed diagnoses were caused by the T. mentagrophytes complex (6.5%), Aspergillus (7.0%) and Fusarium (4.5%). Females were significantly more likely to be infected with non-dermatophytes molds (NDMs; OR: 2.0), including Aspergillus (OR: 3.3) and Fusarium (OR: 2.0), and yeasts (OR: 1.5), including Candida albicans (OR: 2.0) and C. parapsilosis (OR 1.6), than males. The T. mentagrophytes complex became more prevalent with age, and conversely the T. rubrum complex became less prevalent with age. Patients aged ≥65 years also demonstrated a higher likelihood of contracting onychomycosis caused by NDMs (OR: 1.6), including Aspergillus (OR: 2.2), Acremonium (OR: 3.5), Scopulariopsis (OR: 2.9), Neoscytalidium (OR: 3.8), and yeasts (OR: 1.8), including C. albicans (OR: 1.9) and C. parapsilosis (OR: 1.7), than young adults. NDMs (e.g., Aspergillus and Fusarium) and yeasts were, overall, more likely to cause superficial onychomycosis and less likely to cause dystrophic onychomycosis than dermatophytes. With regards to subungual onychomycosis, Aspergillus, Scopulariopsis and Neoscytalidium had a similar likelihood as dermatophytes. The advent of molecular diagnostics enabling a timely and accurate pathogen identification can better inform healthcare providers of appropriate treatment selections and develop evidence-based recommendations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".