Single-Point Nail Sampling to Diagnose Onychomycosis Caused by Non-Dermatophyte Molds: Utility of Polymerase Chain Reaction (PCR) and Histopathology
Bibliographic record
Abstract
The three most commonly used methods for diagnosing non-dermatophyte mold (NDM) onychomycosis are culture, polymerase chain reaction (PCR), and histopathology. Toenail samples from 512 patients (1 sample/patient) with suspected onychomycosis were examined using all three diagnostic tests. A statistically significant association was found between PCR and histopathology results, as well as between fungal culture and histopathology results. All PCR-positive and culture-positive dermatophyte samples were confirmed by histopathology. However, 15/116 (12.9%) of culture-positive NDM samples had negative histopathology results, while all PCR-positive NDM samples were confirmed by histopathology. The overall rate of dermatophyte detection was higher using PCR compared to culture (38.9% vs. 11.7%); the lower rate of NDM detection by PCR (11.7% vs. 38.9%) could be attributed to the restriction of the assay design to seven pre-selected targets. When repeat sampling in the clinic is not possible, a combination of NDM detection by PCR and positive histopathology of hyphae may be a proxy for NDM infection, particularly where the NDM occurs without a concomitant dermatophyte. There was a high degree of correlation between negative PCR and negative histopathology. A negative PCR result with negative histopathology findings may be a reliable proxy for the diagnosis of non-fungal dystrophy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".