Sentinel Lymph Node Biopsy in Head and Neck Melanoma: A Review
Bibliographic record
Abstract
The incidence of melanoma in the United States continues to rise. Head and neck melanomas comprise approximately 20% of all primary cutaneous melanomas. Sentinel lymph node (SLN) biopsy (SLNB) has become the standard of care for staging in melanoma. It has a number of advantages, including the addition of prognostic information, accurate staging, and the potential to add completion lymph node dissection (CLND) or adjuvant therapy when indicated. Furthermore, it may allow for the identification of patients who would benefit from inclusion in clinical trials; this advantage may be amplified based on the introduction of novel targeted therapies. SLNB does have some disadvantages in head and neck melanomas. The complex lymphatic drainage and anatomy of the head and neck can result in some technical challenges. SLN positivity rates in head and neck melanoma are lower than for trunk or extremity melanoma; despite this, overall and disease free survival rates are lower in head and neck melanoma. This review examines the literature evidence for the efficacy of SLNB in head and neck melanoma, and in particular attempts to estimate five variables: the likelihood of finding a SLN, the number of SLNs found, the likelihood of a positive SLN, the likelihood of identifying positive non-sentinel lymph nodes on CLND, and the likelihood of recurrence in the neck despite a negative SLNB. Overall, despite the technical challenges inherent in SLNB when applied to head and neck melanoma, it remains a technically feasible and effective procedure in this anatomic site.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".