Patient Characteristics, Diagnostic Testing Utilization, and Antifungal Prescribing Pattern for Onychomycosis in the USA: A Cohort Study Using DataDerm, 2016–2022
Why this work is in the frame
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Bibliographic record
Abstract
Introduction: Onychomycosis is a complex nail disease that is commonly seen in daily practice. Methods: Electronic health records of clinically diagnosed onychomycosis patients were extracted using DataDerm - a dermatology data registry hosted by the American Academy of Dermatology - spanning from the year 2016 to 2022. Results: Regardless of age, an increasing trend in patient volume was observed in the Southern US region, which accounted for 50.7-56.9% of onychomycosis patients in 2022. A coinfection of tinea pedis was present among 15.6-22.5% of patients. Diagnostic testing was infrequently utilized with less than one-quarter of patients having a histopathologic examination (12.7-21.9%) followed by fungal culture (5.5-8.2%) and direct microscopic examination (3.3-6.0%). Treatments were infrequently prescribed, accounting for less than one-quarter of patients (orals, terbinafine: 20.8-29.1%, fluconazole: 12.9-16.5%; topicals, efinaconazole: 3.2-13.8%); over 30% of treated patients received a combination regimen or experienced switching of treatments. Prescribing patterns did not significantly differ in vulnerable patient groups such as elderly patients and in patients with concomitant tinea pedis. Patients receiving a topical and/or oral antifungal prescription were frequently not tested to confirm the onychomycosis diagnosis (76.9%). Conclusion: Our findings add to a growing body of literature calling for the improvement of onychomycosis management practices.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it