A Retrospective Study Comparing K101 Nail Solution as a Monotherapy and in Combination with Oral Terbinafine or Itraconazole for the Treatment of Toenail Onychomycosis
Bibliographic record
Abstract
Background: Onychomycosis is a difficult-to-treat fungal infection of the nails. The efficacy of monotherapy is not ideal, and combination therapies provide an alternative that may increase treatment efficacy. Method: A retrospective analysis of data from 91 patients was undertaken. Treatment for toenail onychomycosis occurred between 2014 and 2016 and consisted of combination therapy with oral terbinafine (250 mg/day for 12 weeks) or itraconazole (3 pulses, 400 mg/day for 7 days) + K101 nail solution daily, or K101 nail solution monotherapy. Efficacy outcomes at 12 and 15 months were analyzed. Results: At 12 months, the clinical cure rate for combination of terbinafine + K101 solution was significantly higher than that for K101 monotherapy (p = 0.008). Patients receiving this combination also showed significant improvement in percent of affected nail at 3 months (p = 0.029), while patients receiving itraconazole + K101 solution demonstrated improvement in percent of affected nail at 6 months (p = 0.037). At 15 months, there was no significant difference between treatments for complete, clinical, and mycological cure. Conclusion: Combination therapy with oral terbinafine or itraconazole and K101 nail solution results in clearance of infected nail earlier than that with topical K101 alone. These combinations may encourage compliance and be effective for patients with moderate onychomycosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".