Dermatophytomas: Clinical Overview and Treatment
Bibliographic record
Abstract
Dermatophytomas are characterized as a hyperkeratotic fungal mass in the subungual space, showing as dense white or yellow, typically in longitudinal streaks or patches. Masses can be visualized by traditional microscopy or histology. Newer technologies such as dermoscopy and optical coherence tomography also provide visual features for dermatophytoma diagnosis. The density of fungal mass, and lack of adherence to the nail structures, as well as possible biofilm development, may play a role in the reduction in drug penetration and subsequent lack of efficacy with traditional oral therapies such as terbinafine and itraconazole. A combination of drug treatment with mechanical or chemical debridement/avulsion has been recommended to increase efficacy. The topical antifungal solutions such as tavaborole, efinaconazole, and luliconazole may reach the dermatophytoma by both the transungual and subungual routes, due to low affinity for keratin and low surface tension. Current data indicates these topicals may provide efficacy for dermatophytoma treatment without debridement/avulsion. Similarly, fosravuconazole (F-RVCZ) has an improved pharmacological profile versus ravuconazole and may be an improved treatment option versus traditional oral therapies. The availability of improved treatments for dermatophytomas is crucial, as resistance to traditional therapies is on the increase.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".