Treatment of Cutaneous Melanoma of the Face by Mohs Micrographic Surgery
Bibliographic record
Abstract
BACKGROUND: The treatment of cutaneous malignant melanoma of the face presents a challenge to ensure eradication of disease with maximum preservation of tissue. Mohs micrographic surgery provides a means for histologically controlled removal of malignant melanoma. OBJECTIVE: This study evaluates the efficacy of Mohs micrographic surgery, at a single institution, for the treatment of facial melanoma and assesses the accuracy of margin control by frozen section techniques. METHODS: Ninety-seven patients with biopsy-confirmed melanoma in situ or invasive melanoma of the face were treated by Mohs micrographic surgery over a 6-year period. In 25 patients, tissue margins defined as negative for melanoma at the time of frozen section were re-evaluated on permanent section histology of formalin-fixed, paraffin-embedded tissue. RESULTS: Ninety-two of 97 patients had followup information available (8-72 months; mean 33 months). There were no cases of local recurrence. Eighty-nine of the 92 patients were alive and well with no evidence of disease. One patient died of metastatic melanoma. In situ or invasive melanoma was not identified on permanent sections of 117 tissue margins which had been interpreted as negative on frozen section. CONCLUSION: Mohs micrographic surgery appears to be an effective treatment for facial melanomas. Our study showed complete correlation between frozen section tissue margins and permanent section controls.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".