Bibliographic record
Abstract
Recent studies have reported an increase in pediatric onychomycosis prevalence worldwide, suggesting that this population may be increasingly affected by the infection. A summary of the epidemiological impact, antifungal treatment options, special considerations for at-risk subpopulations, and methods to prevent infection and recurrence are discussed. A systematic review of available epidemiological studies found the worldwide prevalence of culture-confirmed pediatric toenail onychomycosis to be 0.33%, with no significant increases in prevalence over time. A systematic review of studies investigating the efficacy of various antifungals in treating pediatric onychomycosis found high cure rates and low frequency of adverse events with systemic itraconazole and terbinafine; however, the studies are few, dated, and lack impact because of small sample sizes. Comparatively, clinical trials implementing FDA-approved topical antifungal treatments report slightly reduced cure rates with larger sample sizes. Patients with immunity-altering conditions, such as Down's syndrome, or those immunosuppressed because of chemotherapy or HIV/AIDS are at a greater risk of onychomycosis infection and require special consideration with treatment. Proper sanitization and hygiene practices are necessary to reduce the risk of acquiring infection. Early diagnosis and treatment of onychomycosis in children, as well as any affected close contacts, are crucial in reducing the impact of the disease.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.005 | 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".