Onychomycosis in special populations
Bibliographic record
Abstract
Onychomycosis, a common fungal nail infection, can present unique diagnostic and therapeutic challenges in special populations including the elderly, children, individuals with diabetes, immunocompromised patients, and those with compromised organ function or psoriasis. These groups face increased susceptibility due to factors such as impaired immunity, vascular insufficiency, comorbidities, and altered nail morphology. Despite its often-benign perception, untreated onychomycosis in these populations can lead to complications, including secondary infection, ulceration, and systemic spread. Accurate diagnosis, requiring mycological confirmation, is important as clinical features may overlap with non-fungal nail disorders. Treatment must be tailored to individual risk profiles, with careful consideration of drug efficacy, safety, and potential interactions with comorbidities and polypharmacy. While systemic antifungals remain the standard for moderate-to-severe disease, their use may be limited by comorbidities, such as renal and hepatic impairment, and drug interactions. Topical antifungals such as efinaconazole, tavaborole, and ciclopirox can be considered for mild to moderate onychomycosis. This review provides an in-depth overview of the epidemiology, diagnosis, and management of onychomycosis in special populations, underscoring the importance of individualized care to improve outcomes and reduce recurrence.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".