Epidemiological trends and healthcare disparities in onychomycosis: An analysis of the All of Us research program
Bibliographic record
Abstract
Onychomycosis is a common, difficult to treat nail disorder. Our objective was to explore disparities in current clinical management practices for onychomycosis in patients from underrepresented groups and with specific comorbidities. We conducted a cross-sectional study using the All of Us (AoU) research program. The AoU program gathers survey, and electronic health records from participants in the United States with the aim of increasing the representation of minorities groups in health research under the framework of precision medicine. We identified 18,763 onychomycosis patients (2017-2022) and compared the rates of diagnostic testing, prescription medications and surgical procedures. Younger patients were more likely to receive oral medications, while older patients were more likely to undergo surgical nail procedures. Patients with lower income and education, Black and Hispanic patients were less likely to receive testing to confirm diagnosis, and less likely to receive prescription medications (topical and/or oral) except in the case of fluconazole. Lower income and education were associated with a higher likelihood of debridement and trimming procedures, while Black and Hispanic patients were less likely to undergo these procedures. Patients with disabilities also received different treatments when compared to able-bodied individuals, being less likely to receive ciclopirox, efinaconazole and terbinafine, but more likely to undergo debridement and trimming procedures. There are clear differences in the management of onychomycosis in the different demographic and comorbid populations that we studied. Efforts to reduce these inequalities, such as expanded health coverage, reducing communication barriers and increasing patient and physician education are needed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".