Implications of tumor-positive sentinel lymph nodes in single vs multiple nodal basins in melanoma
Bibliographic record
Abstract
Background: Melanoma patients' prognosis is based on the primary tumor characteristics and the tumor status of the regional lymph nodes. The advent of lymphoscintigraphy with SLN biopsy (SLNB) has shown that melanoma can drain to multiple nodal basins but the significance of multiple basins (vs. one basin) with tumor-positive sentinel lymph node(s) (+SLN) of similar tumor burden has not been shown. We examined the impact of the number of nodal basins with +SLN (+basin) in melanoma patients and its significance for patients' prognosis and survival. Study design: We identified 1,915 patients with +SLN from two randomized surgical clinical trials: Multicenter Selective Lymphadenectomy Trials I and II. Patient groups were divided based on number of +SLNs and number of +basins. Disease-free survival (DFS), distant disease-free survival (DDFS) and melanoma-specific survival (MSS) were compared with the Kaplan-Meier method and log-rank tests. Univariable and multivariable analyses were performed using Cox proportional hazard regressions. Results: Among the 1,915 patients, 1,501 had only one +SLN (78%) in one basin and 414 (22%) had multiple +SLNs: 340 located in one basin and 74 in multiple basins. Among patients with multiple +SLNs, those with multiple +basins have a worse DFS, DDFS and MSS than those with a single basin (p ≤ 0.0001 for all comparisons). MSS was significantly different based on AJCC stages: AJCC IIIA and IIIB (p ≤ 0.001 and 0.0287, respectively). Conclusion: Our results suggest that the number of tumor-positive basins may be important for staging and in understanding the biology of lymph node metastases.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".