Molecular Determination of Mixed Infections of Dermatophytes and Nondermatophyte Moulds in individuals with Onychomycosis
Bibliographic record
Abstract
Abstract Background: Reports of mixed infections with NDMs and dermatophytes in onychomycosis are rare, possibly due to the inhibition of NDM growth during traditional culture. Objective: To determine the prevalence of mixed infections in onychomycosis using molecular identification. Methods: Molecular analyses were utilised to identify infecting organisms directly from at least two serial great toenail samples from each of the 44 subjects. Results: Mixed infections were present in 41% (18/44) of the subjects. A single co-infecting NDM was the most common mixed infection and was detected in 34% (15/44) of onychomycosis patients, with F. oxysporum present in 14% (6/44), S. brevicaulis in 9% (4/44), Acremonium spp in 2% (1/44), Aspergillus spp. in 4.5% (2/44) and Scytalidium spp. in 4.5% (2/44) of patients. Mixed infections with two NDMs were found in 7% (3/44) of the subjects. Conclusions: Mixed dermatophyte/NDM onychomycosis infections may be more prevalent than previously reported.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".