High prevalence of mixed infections in global onychomycosis
Bibliographic record
Abstract
Onychomycosis is estimated at a prevalence of 10% worldwide with the infecting organism most commonly Trichophyton rubrum (T. rubrum). Traditional culture identification of causative organisms has inherent risks of overestimating dermatophytes, like T. rubrum, by inhibiting the growth of possible nondermatophyte mould (NDM) environmental contaminants which could be causative agents. Recently, molecular methods have revealed that a proportion of onychomycosis cases in North America may be caused by mixed infections of T. rubrum as an agent co-infecting with one or more NDM. Determining the global burden of mixed infections is a necessary step to evaluating the best therapies for this difficult-to-treat disease. To determine the prevalence of mixed infections in a global population, nail samples from onychomycosis patients in Brazil, Canada, and Israel (n = 216) were analyzed by molecular methods for the presence of dermatophytes and five NDMs. If an NDM was detected, repeat sampling was performed to confirm the NDM. T. rubrum was detected in 98% (211/216) of infections with 39% mixed (84/216). The infection type was more likely to be mixed in samples from Brazil, but more likely to be a dermatophyte in samples from Canada and Israel (Χ2 = 16.92, df = 2, P<0.001). The most common cause of onychomycosis was T. rubrum. In all countries (Brazil, Canada and Israel combined) the prevalence of dermatophyte (Χ2 = 211.15, df = 3, P<0.001) and mixed (dermatophyte and NDM; Χ2 = 166.38, df = 3, P<0.001) infection increased with patient age. Our data suggest that mixed infection onychomycosis is more prevalent than previously reported with the aging population being at increased risk for mixed infections.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".