Invasive melanoma of the face: Management, outcomes, and the role of sentinel lymph node biopsy in 260 patients at a single institution
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The face is a common site of melanoma occurrence. The purpose of this study was to examine the management and outcomes of patients with invasive melanoma of the face. METHODS: Patients with invasive melanoma of the face managed at our institution from 1997 to 2008 were retrospectively reviewed. Details of sentinel lymph node biopsy (SNB), disease recurrence, and deaths were recorded. RESULTS: Two hundred sixty patients were reviewed (mean age 68, mean tumor thickness 0.87 mm). Of 100 patients eligible for SNB (tumor thickness ≥ 1 mm, Clark level ≥ IV, or ulceration) this was performed in only 29 (29%), and those who underwent SNB were younger than those who did not (mean age 59 vs. 79 years, P < 0.0001). SNB was successful in 28 (97%), and no complications occurred. SNB was positive in 3 (11%). After mean follow-up of 30 months, nodal recurrence occurred in 9 (3.5%) and distant recurrence in 20 (7.7%). There were 60 deaths (overall mortality 23%); attributed to melanoma in only 16 cases (disease specific mortality 6.2%). CONCLUSIONS: Facial melanoma is associated with low rates of regional recurrence despite underutilization of SNB. Older patients are less likely to undergo SNB. Due to the advanced age of patients with facial melanoma, most deaths occurring are from unrelated causes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".