Patient Characteristics, Diagnostic Testing Utilization, and Antifungal Prescribing Pattern for Onychomycosis in the USA: A Cohort Study Using DataDerm, 2016–2022
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".