Onychomycosis in children – review on treatment and management strategies
Bibliographic record
Abstract
BACKGROUND: Onychomycosis is an uncommon condition in children with increasing global prevalence. Health practitioners should confirm the diagnosis through mycology examination and examine family members of affected individuals for onychomycosis and tinea pedis. OBJECTIVE: To comprehensively summarize the treatment and management strategies for pediatric onychomycosis. METHODS: We performed a comprehensive literature search in the PubMed database to identify clinical studies on treatment for mycologically-confirmed dermatophyte onychomycosis in children <18 years. The exclusion criteria were combination therapy, case reports, reviews, systematic reviews and duplicate studies. RESULTS: Per-weight dosing regimens of systemic antifungal agents such as terbinafine, itraconazole, and fluconazole are found to be safe in children and are used off-label for the treatment of pediatric onychomycosis with high efficacy. Topical antifungal agents such as ciclopirox, efinaconazole, and tavaborole have established safety and efficacy in children. Children respond better than adults to topical therapy due to their thinner, faster growing nails. There is no data on the efficacy of medical devices for onychomycosis in children. CONCLUSION: Efinaconazole topical solution 10% and tavaborole topical solution 5% are FDA approved for the treatment of onychomycosis in children ≥6 years; ciclopirox topical solution 8% nail lacquer is approved in children ≥12 years.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".