Bone metastases affect prognosis but not effectiveness of third-line targeted therapies in patients with metastatic renal cell carcinoma
Bibliographic record
Abstract
INTRODUCTION: Treatment of metastatic renal cell carcinoma (mRCC) has improved with the use of targeted therapies, but bone metastases continue to be negative prognostic factor. METHODS: Patients with mRCC treated with everolimus (EV) or sorafenib (SO) after two previous lines of targeted therapies were included in the analysis. Overall survival (OS) and progression-free survival (PFS) were assessed based on the presence of bone metastases and type of therapy; they were also adjusted based on prognostic criteria. RESULTS: Of the 233 patients with mRCC, 76 had bone metastases. Of the 233 patients, EV and SO were administered in 143 and 90 patients, respectively. Median OS was 10.4 months in patients with BMs and 17.4 months in patients without bone metastases (p = 0.002). EV decreased the risk of death by 18% compared to SO (adjusted hazard ratio [HR] 0.82, 95% confidence interval [CI] 0.74-0.91; p < 0.001), with comparable effects in patients with or without bone metastases. In the same manner, EV decreased the risk of progression by 12% compared to SO (adjusted HR 0.88, 95% CI 0.82-0.96; p = 0.002), but this difference was not significant in patients without bone metastases. The major limitations of the study are its retrospective nature, the heterogeneity of the methods to detect bone metastases, and the lack of data about patients treated with bisphosphonates. CONCLUSIONS: The relative benefit of targeted therapies in mRCC is not affected by the presence of bone metastases, but patients without bone metastases have longer response to therapy and overall survival.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".