Treatments for Onychomycosis: A Bibliometric Analysis
Bibliographic record
Abstract
Introduction: Oral antifungals were the earliest treatments to receive approval for the management of onychomycosis and have a long-standing record to support their efficacy. Topical antifungals and device-based treatments have been explored and some implemented in more recent years as alternatives to traditional oral antifungals. The present bibliometric analysis summarizes trends in publication frequency for onychomycosis treatment modalities over time and characterizes their body of literature in terms of types of studies available and relative level of evidence. Methods: A comprehensive literature search was performed using Web of Science and SCOPUS databases. Results: Covering all publications from 1970 to present day, our search identified oral therapeutics n = 295 articles (n = 63 randomized control trials [RCTs]), topical therapeutics n = 358 articles (n = 72 RCTs), and device-based treatments n = 158 articles (n = 37 RCTs). Spikes in research activity surround FDA approval of therapeutics for each treatment modality. Research activity within the last decade has focused on topical and device-based treatments. Evidence for efficacy of device-based treatments is lacking from relatively few high-quality RCTs. Conclusion: With growing concern for non-dermatophyte mold onychomycosis and terbinafine resistance, researchers should validate the efficacy and safety of device-based treatments with high-quality studies.
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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 | 0.016 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.232 | 0.256 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".