Recalcitrant dermatophytosis: clinicomycological features and challenges in management
Bibliographic record
Abstract
INTRODUCTION: Dermatophytosis is the most common fungal infection encountered by primary care providers and outpatient physicians. In recent years, new patient populations with chronic infections - accompanied by a history of recurrences and relapses - presenting with unusual or severe manifestations have been reported worldwide. This is broadly referred to as recalcitrant dermatophytosis. AREAS COVERED: Through an electronic literature search spanning the last 25 years, we discuss systemic treatment options for recalcitrant dermatophytosis, including conventional terbinafine and itraconazole treatments, either alone or in combination with topicals. Dosing strategies and treatment durations are summarized along with potential reasons for treatment failure. There is mounting evidence suggesting that dermatophyte resistance is a significant cause of terbinafine nonresponse, making itraconazole the preferred first-line treatment. However, pharmacokinetic variability may cause sub-therapeutic exposure and induce resistance to itraconazole. In some instances, super-bioavailable itraconazole may be a consideration. Corticosteroids should be strictly avoided. EXPERT OPINION: The current literature is limited by case reports and small case series. Newer triazoles and ketoconazole have been reported as drugs of last resort. Increased advocacy and collaboration are needed to standardize the management of recalcitrant dermatophytosis including antifungal susceptibility testing, especially concerning special populations such as pregnant individuals and children.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".