Multitherapy Approach to Onychomycosis Therapy
Bibliographic record
Abstract
New medications and new formulations have provided an increase in the cure rates for onychomycosis. Many cases of infection, however, are still not cured. It is not always obvious which factors are most relevant to reduction of cure, and factors may vary with each patient. For these reasons, a multitherapy approach to onychomycosis may be needed to individualize treatment to each patient's specific condition. Different presentations and severity levels of onychomycosis may respond differently to treatment modalities and require varying amounts of intervention. Nail débridement may be used to lessen the burden of infection in cases in which drug penetration may not occur adequately otherwise, such as dermatophytoma, onycholysis, or lateral infection. Ciclopirox nail lacquer has been approved for use in conjunction with regular débridement and represents the first approved multitherapy approach. Topical antifungals may be combined with oral antifungals to provide dual fronts of drug penetration. Similarly, two oral medications may be combined to provide a wider spectrum of antifungal activity and differential mode of action against the organisms, which may increase fungistatic or fungicidal action. There is a nonclinical component of therapy, represented by patient education on onychomycosis infection and treatment, which should be used to ensure that patient expectations are realistic and to encourage patient compliance with the chosen regimens.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".