Cytoreductive nephrectomy in metastatic renal cell carcinoma: the evolving role of surgery in the era of molecular targeted therapy
Bibliographic record
Abstract
PURPOSE OF REVIEW: Despite contemporary surgical and therapeutic innovation, renal cell carcinoma remains the most lethal of the urologic malignancies. Up to a third of patients with renal cell carcinoma have metastatic disease at presentation and 30% with localized disease will eventually progress to metastatic disease. In the past, cytoreductive nephrectomy was reserved for palliative circumstances. RECENT FINDINGS: With the emergence and integration of targeted therapies into current treatment protocols, the role of cytoreductive nephrectomy should be reexamined. Three targeted therapy trials revolutionized the management of metastatic renal cell carcinoma by showing unprecedented efficacy and tolerable therapeutic options. SUMMARY: The benefits of targeted therapy observed in clinical trials have been in the setting of prior cytoreductive nephrectomy. Therefore, any evidence for the potential benefit of cytoreductive nephrectomy in the era of targeted therapy is currently extrapolated and it is unclear where surgery integrates into the current treatment ladder. Other surgical approaches such as laparoscopy and nephron-sparing surgery have also been transferred to a carefully selected metastatic disease population. The concept of presurgical systemic therapy, though not new to oncology, is novel for metastatic renal cell carcinoma treated with targeted therapy. With the maturation of ongoing multicenter clinical trials, answers to these questions will hopefully be clarified.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".