Comparison of Outcomes for Malignant Melanoma of the Face Treated Using Mohs Micrographic Surgery and Wide Local Excision
Bibliographic record
Abstract
BACKGROUND: Mohs micrographic surgery (MMS) is an accepted treatment for nonmelanoma skin cancer and has an evolving role in melanoma. OBJECTIVE: To review oncologic outcomes of MMS and wide local excision (WLE) treatments for facial melanoma. METHODS AND MATERIALS: A retrospective review of patients with invasive melanoma of the face between 1997 and 2007 identified from the Alberta Cancer Registry (Canada) was performed. Outcome measures were local recurrence (recurrence <2 cm from excision scar), distant recurrence (regional or systemic), and disease-specific survival. RESULTS: One hundred fifty-one patients were available for analysis (60 MMS, 91 WLE). Median follow-up time was 48 months. The groups differed in tumor location and mitotic rate. Overall, there was no significant difference in 5-year local recurrence (7.9% WLE vs 6.2% MMS, p = .58), regional or systemic recurrence (18.8% vs 8.8%, p = 0.37) or disease-specific survival (82.8% vs 92.4%, p = .59). Breslow thickness was the only consistent predictor of local recurrence or other recurrence and disease-specific survival on multivariate analysis. Subset analysis of tumors with Breslow thickness less than 2 mm did not reveal any difference in outcomes. CONCLUSION: Mohs micrographic surgery has oncologic outcomes of local recurrence, distant recurrence and overall survival similar to those of WLE for invasive facial melanoma.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".