Evaluating the Prognostic Variables for Overall Survival in Patients with Metastatic Renal Cell Carcinoma: A Meta-Analysis Of 29,366 Patients
Bibliographic record
Abstract
Background: Scoring systems are a method of risk assessment used to stratify patients with metastatic renal cell carcinoma (mRCC) and guide systemic therapy. The variables are weighed equally when calculating total score. However, the difference of even 1 positive predictor can change one's risk category and therapy. Objective: To compare the relative strength of association between predictive variables and overall survival (OS) in mRCC. Methods: A search of Medical Literature Analysis and Retrieval System Online (MEDLINE) and Embase was conducted. Clinical studies, retrospective and prospective, were included if the association of at least 1 predictor and OS in patients with mRCC receiving first-line systemic therapy was evaluated. Meta-analysis was performed to generate pooled hazard ratios (HRs) and 95% CIs for OS for predictors with ≥ 5 included studies. Sensitivity analysis identified outlier heterogeneity and publication bias. Results: Sixty-six studies containing 29,366 patients were included. Meta-analysis indicated lung metastases, bone metastases, thrombocytosis, time to systemic therapy < 1 year, liver metastases, hypercalcemia, anemia, elevated neutrophil-lymphocyte ratio, multiple metastatic sites, neutrophilia, poor Eastern Cooperative Oncology Group (ECOG) status, no previous nephrectomy, elevated lactate dehydrogenase, Fuhrman grade 3 or 4, central nervous system metastases, elevated C-reactive protein, and Karnofsky Performance Status < 80% were associated with significantly worse OS. The HRs varied from 1.34 to 2.76, representing heterogeneity in predictive strength. The effects of study heterogeneity and publication bias were minimal to moderate across all predictors. Conclusions: Based on the differences in pooled HRs, prognostic strength between the variables is likely not equivalent. Restructuring scoring models, through inclusion of other variables and usage of relative weighting, should be considered to improve accuracy of risk stratification.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.048 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".