Focus on the dabrafenib, vemurafenib, and trametinib in clinical outcome of melanoma: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Melanoma is the most serious lethal skin cancer, affects the melanin producer cells (melanocytes). Surgery is the most common treatment, whereas for the advance stage the development of a treatment is recommended. BRAF (Dabrafenib and Vemurafenib) inhibitor or MEK inhibitor (Trametinib) is used as the most frequently targeted therapy of melanoma due to more than 80% patient 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 3 different assessors. Data analysis was carried out by using Review Manager (RevMan 5.4). Heterogenicity test was assessed by I2 and Chi2 statisticResult: There are 20 studies used in this article (13 RCT and 7 cohorts). The overall survival (OS) and progression-free survival (PFS) of study that using targeted therapy (vemurafenib, trametinib, or dabrafenib) compare other therapies (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 therapy for a better clinical outcome rather than monotherapy.Keyword: melanoma, dabrafenib, vemurafenib, and trametinib
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.008 | 0.010 |
| 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) | 0.005 | 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".