Mesenchymal–Epithelial Transition Kinase Inhibitor Therapy in Patients with Advanced Papillary Renal-Cell Carcinoma: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Papillary subtypes of renal-cell carcinoma (pRCC) represent 10–15% of the cases and commonly have MET alterations. This systematic review and single-arm meta-analysis evaluated MET inhibitor therapy (METi) efficacy and safety in adults with confirmed advanced pRCC. The search strategy included PubMed, Web-of-science, Cochrane, and Scopus. We used the DerSimonian/Laird random effect model for all analyses; p-value < 5% was considered significant, and heterogeneity was assessed with I2. Three clinical trials and six cohort studies were included with 504 patients; 31% were MET-driven. Our pooled analysis demonstrated an objective response rate (ORR) in MET-driven, MET-independent, and overall patients of: 36% (95%CI: 10–62), 0% (95%CI: 0–3), and 21% (95%CI: 1–41), respectively. One-year disease control and progression-free survival rates were, respectively, 70% (95%CI: 52–88) and 15% (95%CI: 10–20). Twelve- and twenty-four-month survival rates were, respectively, 43% (95%CI: 23–64) and 10% (95%CI: 0–30). The prevalence of adverse events of any grade and grades 3–5 were 96% (95%CI: 91–100) and 44% (95%CI: 37–50), respectively. We suggest METi has anti-tumor activity and is tolerable in patients with advanced pRCC.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".