Current Advancements and Novel Strategies in the Treatment of Metastatic Melanoma
Bibliographic record
Abstract
Melanoma is the deadliest form of skin cancer in the world with a growing incidence in North America. Contemporary treatments for melanoma include surgical resection, chemotherapy, and radiotherapy. However, apart from resection in early melanoma, the prognosis of patients using these treatments is typically poor. In the past decade, there have been significant advancements in melanoma therapies. Immunotherapies such as ipilimumab and targeted therapies such as vemurafenib have emerged as a promising option for patients as seen in both scientific and clinical research. Furthermore, combination therapies are starting to be administered in the form of polychemotherapy, polyimmunotherapy, and biochemotherapy, of which some have shown promising outcomes in relative efficacy and safety due to their multiple targets. Alongside these treatments, new research has been conducted into the evidence-based use of natural health products (NHPs) and natural compounds (NCs) on melanoma which may provide a long-term and non-toxic form of complementary therapy. Nevertheless, there is a limited consolidation of the research conducted in emerging melanoma treatments which may be useful for researchers and clinicians. Thus, this review attempts to evaluate the therapeutic efficacy of current advancements in metastatic melanoma treatment by surveying new research into the molecular and cellular basis of treatments along with their clinical efficacy. In addition, this review aims to elucidate novel strategies that are currently being used and have the potential to be used in the future.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".