The safety and efficacy of dabrafenib plus trametinib for patients with brain metastatic melanoma: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Brain metastases (BM) are common complications of metastatic cancer and typically occur in patients with melanoma. This study aims to investigate the dabrafenib plus trametinib for patients diagnosed with melanoma brain metastasis (MBM). METHOD: This review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). PubMed, Embase, Scopus, and Web of Science were searched until January 1, 2025. Data on the neurological progression-free survival (PFS), overall survival (OS), radiological response rate, whether intracranial and total, and adverse events were collected. Quality assessment of the studies was conducted using the Newcastle-Ottawa Scale (NOS). The STATA version 17.0. has been used for analysis of the outcomes. RESULTS: Eleven studies met the inclusion criteria. Our results showed pooled 6-month OS rate of 76% (95% CI [69-84%]), 1-year OS of 45% (95% CI [38-51%]), 6-month PFS rate of 46% (95% CI [40-52%]), 1-year PFS of 22% (95% CI [13-30%]), disease control rate (DCR) of 71% (95% CI [61-80%]), overall response rate (ORR) of 45% (95% CI [33-57%]), complete response rate (CRR) of 4% (95% CI [0-11%]), partial response rate (PRR) of 47% (95% CI [31-63%]), progressive disease rate (PDR) of 29% (95% CI [13-44%]), and stable disease rate (SDR) of 21% (95% CI [14-27%]). The pooled intracranial CRR, intracranial PRR, intracranial SDR, and intracranial PDR were 4% [95% CI: 1-8%], 42% [95% CI: 32-53%], 24% [95% CI: 18-31%], and 24% [95% CI: 13-34%], respectively. CONCLUSION: These findings underscore the effectiveness of dabrafenib plus trametinib in managing MBM, offering potential benefits in disease control and patient outcomes.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.042 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".