Adjuvant Systemic Therapies for Resected Stages III and IV Melanoma: A Multi-Center Retrospective Clinical Study
Bibliographic record
Abstract
BACKGROUND: Adjuvant therapies have been approved for resected melanoma based on improved recurrence-free survival. We present early findings from a real-world study on adjuvant treatments for melanoma. METHODS: A comprehensive chart review was conducted for patients receiving adjuvant systemic therapy for resected high-risk stages III and IV melanoma. Statistical analysis was performed to assess recurrence-free survival and subgroup differences. RESULTS: A total of 149 patients (median age = 58.0 years, 61.1% men, 49.7% with BRAF V600E/K genotypes) were included, with 94.6% having resected stage III melanoma. Anti-PD-1 immunotherapy was received by 86.5% of patients, while 13.4% received BRAF-targeted therapy. At a median follow-up of 22.4 months, the recurrence rate was 31.5%, with 1-year and 2-year recurrence-free survival rates of 79% and 62%, respectively. Similar recurrence rates were observed between anti-PD-1 immunotherapy and BRAF-targeted therapy. Long-term toxicity affected 27.4% of patients, with endocrinopathies and late-emergent immune-related adverse events being common. CONCLUSIONS: Real-world adjuvant systemic therapy aligns with clinical trial practice. Recurrence rates remain high despite treatment, and long-term toxicities, including endocrinopathies and chronic inflammatory conditions, are not uncommon.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".