The impact of kidney function on the outcome of metastatic renal cell carcinoma patients treated with vascular endothelial growth factor‐targeted therapy
Bibliographic record
Abstract
BACKGROUND: A study was undertaken to investigate the effect of baseline renal function on treatment outcome in patients treated with vascular endothelial growth factor (VEGF)-targeted therapy for metastatic renal cell carcinoma (mRCC). METHODS: Retrospective data from 6 North American cancer centers (3 US and 3 Canadian) were pooled to identify patients with mRCC treated with VEGF-targeted therapy. Patient characteristics, response rate, time to treatment failure, and overall survival were collected. The Modification of Diet in Renal Disease formula was used at therapy initiation for calculation of glomerular filtration rate (GFR). RESULTS: Five hundred twenty-nine patients with mRCC who received sunitinib (n = 323), sorafenib (n = 165), or bevacizumab (n = 41) were included in this analysis. Patient characteristics included: 74% male, median age 61 years, and median GFR 60.1 mL/min/1.73 m(2) (range, 6.5-174.2). On univariate analysis, patients with a GFR <60 (n = 262) were more likely to have had a previous nephrectomy (P < .0001) and to be older (P < .0001), but were less likely to have poor prognostic features such as anemia (P = .041), hypercalcemia (P = .008), neutrophilia (P = .039), thrombocytosis (P < .0001), short diagnosis to treatment interval (P = .007), and low Karnofsky performance status (P = .051). GFR <60, when adjusted for poor risk factors, did not have an impact on type of objective response (odds ratio, 1.31; 95% confidence interval [CI], 0.74-2.32; P = .359), time to treatment failure (hazard ratio [HR], 0.97; 95% CI, 0.79-1.19; P = .772), or overall survival (HR, 0.90; 95% CI, 0.69-1.17; P = .439). CONCLUSIONS: Renal function at therapy initiation does not adversely affect the efficacy of VEGF-targeted therapy in mRCC. Clinicians should not avoid treating patients with impaired baseline renal function.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".