Treating onychomycosis in diabetic patients: risk, therapy, and topical opportunity
Bibliographic record
Abstract
Onychomycosis is a prevalent fungal infection of the nail unit that disproportionately affects individuals with diabetes, often presenting with increased severity and a higher risk of complications such as impaired mobility, secondary bacterial infection, and ulceration. OBJECTIVES: This review examines the pathophysiological relationship between diabetes and onychomycosis, appraises the safety and efficacy profiles of current antifungal therapies, and outlines key considerations in selecting appropriate treatment regimens. It addresses pharmacokinetics, hepatic and renal considerations, and drug-drug interactions, while emphasizing the emerging role of topical therapies and the importance of a patient-centered approach. RESULTS: Early and accurate diagnosis utilizing mycological and molecular techniques such as fungal culture, histopathology, and polymerase chain reaction is essential to guide effective management in this high-risk population and mitigate adverse outcomes. Timely intervention is critical to reduce morbidity and prevent long-term complications. Topical and oral antifungals should be considered for mild to moderate and moderate to severe onychomycosis, respectively. The use of oral agents can be complicated by comorbidities and polypharmacy which heighten the risk of contraindications and drug-drug interactions. Therapeutic success in this population relies not only on appropriate drug selection but also on antifungal stewardship and patient adherence to prescribed regimens. CONCLUSIONS: In view of the expanding aging population and increasing comorbidity burden, physicians should remain cognizant about diagnosing and treating onychomycosis in diabetic patients. An interdisciplinary approach to management is advisable.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".