Survival Correlates With Adjuvant Choice in Sentinel Node Positive Head and Neck Cutaneous Melanoma
Bibliographic record
Abstract
OBJECTIVE(S): The objective of this study is to evaluate the utilization and outcomes of completion lymph node dissection (CLND) and immunotherapy for sentinel lymph node biopsy (SLNB) positive head and neck cutaneous melanoma (HNCM). METHODS: Patients with primary HNCM and positive SLNB in the 2020 National Cancer Database (NCDB) Melanoma file were reviewed. The frequency of CLND and immunotherapy was tracked from 2012 to 2019. Clinicodemographic features of patients were evaluated with respect to their post-SLNB treatment choice. Overall survival (OS) was calculated from the time of diagnosis, and the association of therapy choice with survival was determined using a multivariate Cox regression analysis. RESULTS: The rates of CLND declined from 66% to 18% while adjuvant immunotherapy increased to a peak of approximately 40%, with an inflection point occurring in 2016. Multivariate survival analysis indicated that immunotherapy use alone, though not CLND, was associated with improved prognosis (hazard ratio 0.65, 95% confidence interval 0.45-0.93). Patient characteristics associated with immunotherapy administration included age (p < 0.01), insurance type (p = 0.002), income (p < 0.001), and healthcare facility type (p = 0.005). CONCLUSION: In this retrospective NCDB-based study, we find that the modern management of SLNB-positive patients has shifted towards greater use of adjuvant immunotherapy and a decline in CLND; the use of immunotherapy is associated with improved OS. Patients treated with immunotherapy were more likely to be younger, of higher income, and with private health insurance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".