Mohs micrographic surgery in the treatment of lentigo maligna and melanoma
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The treatment of lentigo maligna (LM) and lentigo maligna melanoma (LMM) is challenging due to lesion location, size, patient age, and potential for recurrence and spread. The largest studies to date confirm that for melanocytic tumours, MMS provides high local control rates while minimizing tissue loss. Herein we report our local control rate for melanoma treated by MMS over a decade. METHODS: Charts were reviewed on all patients with melanocytic tumors treated by a single physician (JPA) using MMS over the time period of 1993-2002. Demographic, surgical and pathological details were recorded. Patients were followed for local, regional and distant recurrences. RESULTS: The patient population was comprised of 199 patients with 202 melanomas. There were 69 invasive lesions, with a mean Breslow depth of 0.92 mm (0.2-3.6 mm). The mean number of levels required to clear the lesions was 2.7 (1-7), resulting in a mean defect size of 11.8 cm2 (0.9-70.7 cm2). Patients with LMM were significantly older (73.2 vs. 66.5 yrs, p = 0.012) and had larger defects after MMS (16.74 cm2 vs. 10.27 cm2) than patients with LM. At a mean follow-up of 29.8 months, there were no local recurrences, four regional recurrences, and two distant recurrences. CONCLUSION: MMS is an effective modality for the clearance of melanocytic tumors.
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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".