Focus on the dabrafenib, vemurafenib, and trametinib in the clinical outcome of melanoma: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Melanoma is the most severe lethal skin cancer, affecting melanin producer cells (melanocytes). Surgery is the most common treatment, whereas, for the advanced stage, the development of treatment is recommended. BRAF (Dabrafenib and Vemurafenib) inhibitor or MEK inhibitor (Trametinib) is the most frequently targeted melanoma therapy due to more than 80% of patients with positive BRAF mutation. In this review, those treatments will be investigated systematically to identify their clinical outcome. Method: This systematic literature review (SLR) was performed from Cochrane, Science Direct, Google Scholar, and Pubmed. Cochrane Risk-of-Bias Tool RoB2 is used to assess RCT studies and New-castle Ottawa Scale Assessment to assess cohort studies by three different assessors. Data analysis was carried out by using Review Manager (RevMan 5.4). Heterogenicity test was assessed by I2 and Chi2 statistic Result: There are 20 studies used in this article (13 RCT and seven cohorts). The overall survival (OS) and progression-free survival (PFS) of the survey that using targeted therapy (vemurafenib, trametinib, or dabrafenib) compare other treatments (chemotherapy, immunotherapy, etc.) showed risk ratio (RR) was 1.12 (95%CI 1.07,1.17; I2=100%; p<0,00001). The OS and PFS with monotherapy compare of vemurafenib, trametinib, or dabrafenib with combination therapy showed RR was 1.09 (95%CI.06,1.13; I2=99%; p<0,00001). Conclusion: BRAF and MEK targeted therapy has a good prognosis for a patient with a positive BRAF gene mutation and could be combined with other treatments for better clinical outcomes rather than monotherapy.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.039 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".