Completion lymph node dissection in patients with sentinel lymph node positive cutaneous head and neck melanoma
Bibliographic record
Abstract
BACKGROUND: Relatively few cutaneous head and neck melanoma (CHNM) patients with were included in the multicenter selective lymphadenectomy trial II (MSLT-II). Our objective was to investigate whether immediate completion lymph node dissection completion of lymph node dissection (CLND) was associated with survival benefit for sentinel lymph node (SLN) positive CHNM using the National Cancer Database. METHODS: SLN positive patients with CHNM from 2012 to 2014 were retrospectively analyzed. Patients were divided into two groups: those who underwent SLN biopsy (SLNB) only versus those who underwent SLNB followed by CLND (SLNB + CLND). The primary outcome was 5-year overall survival (OS). RESULTS: Among 530 SLNB + patients, 342 patients underwent SLNB followed by CLND (SLNB + CLND). The SLNB only group had fewer positive SLN, less advanced pathologic stage, and a lower rate of adjuvant immunotherapy. There was no significant difference in 5-year OS between the two groups (51.0% vs 67%; P = .56). After adjusting for pathologic stage, there remained no difference in 5-year OS among patients with stage IIIA (63.0% vs. 73.6%, P = 0.22) or IIIB/IIIC disease (39.1% vs 57.8%; P = .52). Conclusions Using a large nationwide database, CLND was not shown to be associated with improved OS for patients with SLNB positive CHNM, validating the results of MSLT-II.
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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 | 0.001 |
| 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".