Impact of geographic region on overall survival (OS) in patients with metastatic renal cell carcinoma (mRCC): Results from a pooled clinical trials database.
Bibliographic record
Abstract
4583 Background: Health determinants vary according to the geographic region and may impact the outcomes of mRCC patients treated on clinical trials of targeted therapy. We investigate the OS by geographic region of mRCC patients treated in the targeted therapy era. Methods: We conducted a pooled analysis of mRCC patients treated on phase II and III clinical trials. Clinical characteristics and survival data were collected. Statistical analyses were performed using the Kaplan-Meier method and log-rank test in univariate analysis. Results: Overall, 4736 patients were included in the analysis. Patient characteristics differed according to geographic region (table). No statistically significant differences in OS were observed when comparing US/Canada (USC, reference) to other regions: Latin America (LA), Asia/Oceania/Africa (AOA), and Eastern Europe (EE). OS differed among patients enrolled on trials in the USC compared to Western Europe (WE) (20.3 vs.17.4 months, respectively; HR: 1.15; 95%CI 1.03-1.3 p = 0.015). All grade treatment-related adverse events (AE) were reported more frequently in USC. There were no significant differences in grade 3-5 AEs between groups. Conclusions: We highlight that despite differing baseline characteristics, OS was similar among most geographic regions. Factors such as disease biology, access to care, AE reporting, and quality of care that may contribute to potential differences in outcomes among regions need to be further characterized. [Table: see text]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".