Estimating survival for renal cell carcinoma patients with brain metastases: an update of the Renal Graded Prognostic Assessment tool
Bibliographic record
Abstract
Background: Brain metastases are a common complication of renal cell carcinoma (RCC). Our group previously published the Renal Graded Prognostic Assessment (GPA) tool. In our prior RCC study (n = 286, 1985-2005), we found marked heterogeneity and variation in outcomes. In our recent update in a larger, more contemporary cohort, we identified additional significant prognostic factors. The purpose of this study is to update the original Renal-GPA based on the newly identified prognostic factors. Methods: A multi-institutional retrospective institutional review board-approved database of 711 RCC patients with new brain metastases diagnosed from January 1, 2006 to December 31, 2015 was created. Clinical parameters and treatment were correlated with survival. A revised Renal GPA index was designed by weighting the most significant factors in proportion to their hazard ratios and assigning scores such that the patients with the best and worst prognoses would have a GPA of 4.0 and 0.0, respectively. Results: The 4 most significant factors were Karnofsky performance status, number of brain metastases, extracranial metastases, and hemoglobin. The overall median survival was 12 months. Median survival for GPA groups 0-1.0, 1.5-2.0, 2.5-3, and 3.5-4.0 (% n = 25, 27, 30 and 17) was 4, 12, 17, and 35 months, respectively. Conclusion: The updated Renal GPA is a user-friendly tool that will help clinicians and patients better understand prognosis, individualize clinical decision making and treatment selection, provide a means to compare retrospective literature, and provide more robust stratification of future clinical trials in this heterogeneous population. To simplify use of this tool in daily practice, a free online application is available at brainmetgpa.com.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".