Current State of Adjuvant Therapy for Melanoma: Less Is More, or More Is Better?
Bibliographic record
Abstract
Advances in melanoma treatments over the past decade have changed the course of survival for patients. Several adjuvant therapies have been approved and are now considered standard of care for high-risk patients. These therapies have shown improvements for recurrence-free survival and distant metastases-free survival, but not overall survival, as the data are maturing. The 5-year recurrence-free survival in the COMBI-AD study, which compared dabrafenib and trametinib with placebo, was 65% and 58%, respectively. In the KEYNOTE-054 study, the recurrence-free survival at 3 years was 63.7% versus 41%. Despite these advances, approximately 50% of patients will succumb to their disease. Adjuvant therapy is considered potentially curative and avoids the morbidity of relapsed disease and the poor outcomes seen in metastatic disease. However, the lack of overall survival benefit in clinical trials of patients with high-risk stage II and stage III disease raises the question of whether it is more efficacious to treat when there is residual microscopic disease, or to wait until the disease recurs to avoid treating those who may have been cured by surgery alone. Immunotherapy also has the potential for substantial toxicity that may be lifelong; hence, discussion of risks and benefits of therapy is warranted because there should be less tolerance for substantial toxicity in the adjuvant setting. Adjuvant trials are needed that will integrate biomarkers to allow for better selection of patients who will truly benefit from adjuvant therapy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".