Mortality trends and the impact of lymphadenectomy on survival for renal cell carcinoma patients with distant metastasis
Bibliographic record
Abstract
INTRODUCTION: Current treatment paradigms for metastatic renal cell carcinoma (mRCC) invoke a combination of surgical and systemic therapies. We sought to quantify trends in mortality and performance of lymphadenectomy, as well as impact on survival for patients with mRCC. METHODS: The Surveillance, Epidemiology, and End Results registry (SEER) (1988-2011) identified patients with mRCC. Kaplan-Meier curves and Cox proportional hazards models with competing risks regression were employed to assess survival. RESULTS: 15 060 patients with mRCC were identified, with 6316 (41.9%) undergoing cytoreductive nephrectomy. Mean number of lymph nodes removed was 6.2, with mean 3.3 positive nodes among 1018 (43.9%) patients with positive nodes. Median overall survival (OS) increased from seven to 11 months (1999-2010), and finding a positive node decreased median cancer survival from 22 to nine months. Cancer-specific survival (CSS) showed significant decreases in mortality after 2005 (hazard ratio [HR] 0.71 [0.60-0.83] comparing 2010 to 1990). Lymphadenectomy was associated with decreased OS (HR 1.10 [1.03-1.16]; p=0.002) due to decreased CSS (HR 1.10 [1.04-1.17]; p<0.001) without increase in other-cause mortality (HR 0.94 [0.79-1.11]; p=0.455). However, more extensive lymphadenectomy ≥3 lymph nodes removed did not significantly impact OS or CSS. Number of positive lymph nodes was associated with decreased CSS. CONCLUSIONS: mRCC continues to carry a poor prognosis, but current treatment paradigms have led to modest improvements in OS and CSS in recent years. Lymphadenectomy was found to play a prognostic rather than therapeutic role in the management of mRCC. The performance of lymphadenectomy should be limited based on clinical judgment and better incorporated into randomized trials of new systemic therapies to identify scenarios where implementation may improve 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".