Comparison of Outcomes Between Surveillance Ultrasound and Completion Lymph Node Dissection in Children and Adolescents With Sentinel Lymph Node-Positive Cutaneous Melanoma
Bibliographic record
Abstract
OBJECTIVE: To determine the impact of nodal basin ultrasound (US) surveillance versus completion lymph node dissection (CLND) in children and adolescents with sentinel lymph node (SLN) positive melanoma. BACKGROUND: Treatment for children and adolescents with melanoma are extrapolated from adult trials. However, there is increasing evidence that important clinical and biological differences exist between pediatric and adult melanoma. METHODS: Patients ≤18 years diagnosed with cutaneous melanoma between 2010 and 2020 from 14 pediatric hospitals were included. Data extracted included demographics, histopathology, nodal basin strategies, surveillance intervals, and survival information. RESULTS: Of 252 patients, 90.1% (n=227) underwent SLN biopsy (SLNB), 50.9% (n=115) had at least 1 positive node. A total of 67 patients underwent CLND with 97.0% (n=65/67) performed after a positive SLNB. In contrast, 46 total patients underwent US observation of nodal basins with 78.3% (n=36/46) of these occurring after positive SLNB. Younger patients were more likely to undergo US surveillance (median age 8.5 y) than CLND (median age 11.3 y; P =0.0103). Overall, 8.9% (n=21/235) experienced disease recurrence: 6 primary, 6 nodal, and 9 distant. There was no difference in recurrence (11.1% vs 18.8%; P =0.28) or death from disease (2.2% vs 9.7%; P =0.36) for those who underwent US versus CLND, respectively. CONCLUSIONS: Children and adolescents with cutaneous melanoma frequently have nodal metastases identified by SLN. Recurrence was more common among patients with thicker primary lesions and positive SLN. No significant differences in oncologic outcomes were observed with US surveillance and CLND following the identification of a positive SLN.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".