Role of lymphadenectomy during primary surgery for kidney cancer
Bibliographic record
Abstract
PURPOSE OF REVIEW: Lymph node dissection (LND) during radical nephrectomy (RN) for renal cell carcinoma (RCC) is not considered as a standard. The emergence of robot-assisted surgery and effective immune checkpoint inhibitors (ICI) in recent years may change this and lymph node (LN) staging has become easier and has a clinical impact. In this review, we aimed to reconsider the role of LND today. RECENT FINDINGS: Although the extent of LND has still not been well established, removal of more LN seems to provide better oncologic outcomes for a select group of patients with high-risk factors such as clinical T3-4. Adjuvant therapy using pembrolizumab has been shown to improve disease free survival if complete resection of metastatic lesions as well as the primary site is obtained in combination. Robot assisted RN for localized RCC has been widespread and the studies regarding LND for RCC has been recently appeared. SUMMARY: The staging and surgical benefits and its extent of LND during RN for RCC remains unclear, but it is becoming increasingly important. Technologies that allow an easier LND and adjuvant ICI that improve survival in LN-positive patients are engaging the role of LND, a procedure that was needed, but almost never done, is now indicated sometimes. Now, the goal is to identify the clinical and molecular imaging tools that can help identify with sufficient accuracy who needs a LND and which LNs to remove in a targeted personalized approach.
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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) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 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".