Once Weekly Application of Urea 40% and Bifonazole 1% Leads to Earlier Nail Removal in Onychomycosis
Bibliographic record
Abstract
Introduction: Onychomycosis is a chronic nail fungal infection resulting in nail damage and a decreased quality of life. Chemical avulsion of the nail with urea and bifonazole removes fungally infected debris, increasing antifungal treatment efficacy and penetration. Previous clinical observations describe patients who applied their urea and bifonazole ointment less frequently, achieving earlier nail removal. In this study, we analyzed the relationship between duration of urea and bifonazole application and time to nail avulsion. Methods: χ2 tests, multiple regression analysis, and ANOVA were performed to analyze the similarities between treatment regimens (daily, every 3 days, or once a week), association of regimens or patient characteristics to nail removal, and compare time to nail removal between each regimen, respectively. Results: Daily application of ointment and sealing resulted in an average length of time (±SD) to nail removal of 18.7 days (±6.8 days); once every 3 days resulted in nail removal at 12.7 days (±6.2 days) and once per week at 11 days (±4.46 days) (p < 0.001). Age was the only patient factor that affected duration to nail removal. Conclusion: Once weekly application of ointment with sealing for a 1-week duration is associated with a decrease in time to complete chemical avulsion of the nail by approximately 1 week.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".