Evidence-based optimal fluconazole dosing regimen for onychomycosis treatment
Bibliographic record
Abstract
BACKGROUND: Fluconazole could be an alternative to terbinafine and itraconazole for onychomycosis treatment. However, it is difficult to determine the optimal dosing regimen due to the variability in causative agents, dosing regimens and cure rates in clinical trials. By restricting the data to dermatophyte onychomycosis, we aimed to identify an optimal fluconazole dosing regimen. METHODS: We searched the PubMed, EMBASE and CENTRAL databases and the reference sections of published literature for clinical trials on fluconazole monotherapy for culture-proven dermatophyte onychomycosis. Relationships between fluconazole doses, cure rates and duration of therapy were analyzed. RESULTS: Longer treatments, but not higher weekly fluconazole doses, resulted in better cure rates for toenail, and possibly fingernail, onychomycosis. Consequently, mean mycological and clinical cure rates for treatments lasting 6 months or less and more than 6 months were significantly different for toenail onychomycosis. Clinical studies including participants with nondermatophyte mold, Candida species, or negative culture onychomycosis only used fluconazole therapy for 6 months or less. Thus, the relationship between cure rates and duration of treatment could not be confirmed for all causative agents. CONCLUSION: The lowest dose of 150 mg weekly for more than 6 months is recommended for 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 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".