Outcomes of Staged Excision With Circumferential en Face Margin Control for Lentigo Maligna of the Head and Neck
Bibliographic record
Abstract
BACKGROUND: Treatment practices vary for lentigo maligna (LM). Staged excision with circumferential margin control (SECMC) has the potential to achieve low recurrence rates. OBJECTIVES: To evaluate the clinical outcomes of SECMC using permanent, paraffin-embedded sections and delayed reconstruction. METHODS: We conducted a retrospective, uncontrolled, observational cohort study involving patients who underwent staged excision for LM of the head and neck at Women's College Hospital in Toronto, Canada, from September 2010 to March 2013. Recurrence and infection rates were ascertained from patient charts and postal surveys. RESULTS: One hundred and two patients (45 female, 57 male) were included with a median follow-up time of 1410.5 (IQR 260-1756) days. The median age was 69 (IQR 61-79) years. Approximately one-fifth (21%, 21/102) of patients required greater than 0.5 cm margins to achieve histological clearance. One patient (1/102) upstaged to invasive melanoma based on the initial stage of excision. The infection rate was 6% (6/102) and the 5-year cumulative recurrence rate was 1.4% (95% CI 0.2-9.6%). CONCLUSION: SECMC using permanent sections and delayed reconstruction appears to be a safe and effective treatment method for LM on the head and neck. Randomized trials are needed to help define the optimal treatment.
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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 | 0.000 |
| 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".