Clinical outcomes of the sequential use of pazopanib followed by everolimus for the treatment of metastatic renal cell carcinoma: A multicentre study in Korea
Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to investigate the real-world clinical outcomes of first-line pazopanib and second-line everolimus in Korean patients with metastatic renal cell carcinoma (mRCC). METHODS: Data of patients who had mRCC with clear-cell component between 2001 and 2015 at multiple institutions were collected retrospectively. To be included in the analysis, patients had to meet the following criteria: age ≥18 years; received first-line targeted therapy with pazopanib; and received second-line targeted therapy with everolimus. The primary outcomes included overall survival (OS), progression-free survival (PFS), and adverse events (AEs). RESULT: A total of 36 patients were included in the analysis. The median followup period was 33.5 months (range 17-49.5). The median PFS was eight months (95% confidence interval [CI] 6.4-9.6) after treatment with pazopanib and three months (95% CI 1.9-4.1) with everolimus. The median OS was 27 months (95% CI 16.6-37.4). The median treatment duration was seven months (range 4.3-10.8) after treatment with pazopanib and 3.5 months (range 3-4) with everolimus. Multivariate analysis revealed that the Heng risk criteria were independently associated with OS (p<0.001). Almost every patient experienced some form of AE, the majority of which were mostly mild or moderate in severity. The most common AEs were diarrhea (50%), hypertension (44.4%), and fatigue (41.7%) after treatment with pazopanib, and anemia (47.2%), stomatitis (41.7%), and fatigue (38.9%) with everolimus. CONCLUSIONS: The outcomes for the patients treated with pazopanib followed by everolimus in Korea as observed by us were consistent with those reported by previous studies. The Heng risk criteria were significantly associated with the prognosis of patients with mRCC. AEs were mainly mild to moderate and readily managed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".