The prevalence of unsuspected onychomycosis and its causative organisms in a multicentre Canadian sample of 30 000 patients visiting physicians' offices
Bibliographic record
Abstract
BACKGROUND: Onychomycosis is difficult to treat and a concern for many patients. Prevalence estimates of onychomycosis in North American clinic samples have been higher than what has been reported for general populations. OBJECTIVE: A large, multicentre study was conducted to estimate the prevalence of toenail onychomycosis in the Canadian population. METHODS: Patients were recruited from the offices of three dermatologists and one family physician in Ontario, Canada. Nail samples for mycological testing were obtained from normal and abnormal-looking nails. This sample of 32 193 patients includes our previous published study of 15 000 patients. RESULTS: Abnormal nails were observed in 4350 patients. Of these, the prevalence of culture-confirmed toenail onychomycosis was estimated to be 6.7% (95% CI, 6.41-6.96%). Following sex and age adjustments for the general population, the estimated prevalence of toenail onychomycosis in Canada was 6.4% (95% CI, 6.12%-6.65%). The distribution of fungal organisms in culture-confirmed onychomycosis was 71.9% dermatophytes, 20.4% non-dermatophyte moulds and 7.6% yeasts. Toenail onychomycosis was four times more prevalent in those over the age of 60 years than below the age of 60 years. CONCLUSION: The present data highlights that onychomycosis may be a growing medical concern among ageing patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".