Beyond Skin Tumors: A Systematic Review of Mohs Micrographic Surgery in the Treatment of Deep Cutaneous Fungal Infections
Bibliographic record
Abstract
BACKGROUND: Deep cutaneous fungal infections (DCFIs) can cause significant morbidity in immunocompromised patients and often fail medical and standard surgical treatments because of significant subclinical extension. Although rarely considered in this setting, Mohs micrographic surgery (MMS) offers the advantages of comprehensive margin control and tissue conservation, which may be beneficial in the treatment of DCFIs that have failed standard treatment options. OBJECTIVE: To review the benefits, limitations, and practicality of MMS in patients with DCFIs. METHODS: A systematic review of PubMed and EMBASE was conducted to identify all cases of fungal skin lesions treated with MMS. RESULTS: Eight case reports were identified consisting of a total of 8 patients. A majority of patients had a predisposing comorbidity (75%), with the most common being a solid organ transplant (n = 3, 37.5%). The most commonly diagnosed fungal infection was phaeohyphomycosis (n = 5, 62.5%), followed by mucormycosis (n = 2, 25%). No recurrence or complication post-MMS was noted at a mean follow-up of 11.66 months. CONCLUSION: Although not a first-line treatment, MMS can be considered as an effective treatment alternative for DCFIs in cases of treatment failure and can be particularly helpful in areas where tissue conservation is imperative.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".