Impact of Geographic Regions on Overall Survival in Patients With Metastatic Renal Cell Carcinoma: Results From an International Clinical Trials Database
Bibliographic record
Abstract
PURPOSE: Health determinants vary according to geographic region and may affect the outcomes of patients with metastatic renal cell carcinoma (mRCC) treated during clinical trials of targeted therapy. Here, we investigate the overall survival (OS) of patients with mRCC treated in the era of targeted therapy by geographic region. METHODS: We conducted a pooled analysis of patients with mRCC who were treated during phase II or III clinical trials. Clinical characteristics and survival data were collected. Statistical analyses were performed with the Kaplan-Meier method and log-rank test in univariable analysis. RESULTS: Overall, 4,736 patients were included in the analysis. Patient characteristics differed according to geographic region. No statistically significant differences in OS were observed when the United States/Canada (USC) was compared with the following other regions: Latin America, Asia/Oceania/Africa, and Eastern Europe. In a univariable analysis, OS differed among patients enrolled in trials in USC compared with Western Europe (20.3 v 17.4 months; hazard ratio, 1.15; 95% CI, 1.03 to 1.3; P = .015), but it did not differ in a multivariable analysis. All-grade treatment-related adverse events (AEs) were observed more frequently in USC. There were no significant differences in grade 3 to 5 AEs among groups. CONCLUSION: Despite different baseline characteristics, OS was similar among patients enrolled in clinical trials across different geographic regions. Access to clinical trials as well as disease biology, AE reporting, and quality of care may contribute to potential differences in 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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".