Cytoreductive nephrectomy in metastatic kidney cancer: what do we do now?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Metastatic renal cell carcinoma (mRCC) has traditionally been treated with a combination of targeted systemic therapy and cytoreductive nephrectomy. This approach has recently become a topic of debate, because of new randomized data suggesting a lack of survival benefit for cytoreductive nephrectomy. We review the literature relevant to cytoreductive nephrectomy in the modern era of targeted and immune systemic therapy, and discuss the ongoing role of surgery for treatment of patients with mRCC. RECENT FINDINGS: Randomized trials in the cytokine era of systemic therapy for mRCC demonstrated a survival benefit to cytoreductive nephrectomy, which led to its widespread adoption. There is overwhelming support in favor of cytoreductive nephrectomy from large studies using retrospective data in the targeted therapy era. A recent randomized control trial (CARMENA) failed to show superiority of cytoreductive nephrectomy in combination with sunitinib, versus sunitinib alone with respect to overall survival. The trial had major limitations including selection of many poor-risk patients, which we know do not benefit from surgery. The results of CARMENA should lead to the abandonment of cytoreductive nephrectomy in poor-risk and many intermediate-risk patients with mRCC. However, there is a knowledge gap with respect to the role of cytoreductive nephrectomy in patients with good risk disease, and we argue that these patients should be strongly considered for cytoreductive nephrectomy. SUMMARY: Cytoreductive nephrectomy continues to play an important role in the multidisciplinary management of mRCC; however, diligent patient selection is crucial, as only patients with good risk features are likely to derive benefit from surgery.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".