Clinical Diagnosis and Laboratory Testing of Abnormal Appearing Toenails: A Retrospective Assessment of Confirmatory Testing for Onychomycosis in the United States, 2022–2023
Bibliographic record
Abstract
Onychomycosis is an under-recognized healthcare burden. Despite the risk of misdiagnosis, confirmatory laboratory testing is under-utilized. Histopathologic examination with polymerase chain reaction (PCR) is currently the most effective diagnostic method; it offers direct detection and identification of a fungal invasion. In this retrospective cohort study, we assessed confirmatory testing results, with matching clinical diagnoses, in 96,293 nail specimens submitted during a 9-month period from 2022 to 2023. Toenail specimens were examined using fungal culture, histopathology and/or PCR. Clinical diagnoses were identified using the International Classification of Diseases 10th Revision codes. For clinically diagnosed onychomycosis patients, the overall positivity rate was 59.4%; a similar positivity rate (59.5%) was found in patients with clinically diagnosed non-fungal nail dystrophy. Performing a histopathologic examination with PCR was more likely to provide pathogen identification results than using fungal culture. Male patients had a higher rate of onychomycosis overall; however, female patients had more non-dermatophyte mold onychomycosis caused by Aspergillus. Clinically diagnosed onychomycosis patients with a co-diagnosis of tinea pedis were more likely to test positive for onychomycosis by PCR (odds ratio [OR]: 4.2; 95% confidence interval [CI]: 2.7–6.4), histopathology (OR: 2.5; 95% CI: 2.0–3.1) and fungal culture (OR: 3.2; 95% CI: 1.5–6.6). Our results support the use of confirmatory laboratory testing when there is a clinical diagnosis of onychomycosis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".