Cytoreductive nephron‐sparing surgery does not appear to undermine disease‐specific survival in patients with metastatic renal cell carcinoma
Bibliographic record
Abstract
BACKGROUND: The role of nephron-sparing surgery (NSS) showed promise in patients with metastatic renal cell carcinoma (MRCC). The disease-specific survival of patients with MRCC was compared according to the type of surgery, NSS (N=45) versus radical nephrectomy (RN) (N=732), in unmatched and matched analyses. METHODS: Kaplan-Meier, life tables, log-rank test, and univariate as well as multivariate Cox regression analyses addressed disease-specific survival of NSS versus RN patients. Subsequently, up to 4 RN cases were matched with each NSS case for TNM stage, Fuhrman grade, and histology. Then, disease-specific survival differences were tested with the log-rank statistic. Finally, the sample size necessary to achieve 80% power in survival analyses between the 2 groups (NSS vs RN) was calculated. RESULTS: Of 45 NSS cases, 38 were matched with 99 of 732 RN cases. First, in multivariate unmatched analyses RN predisposes to 1.7-fold higher RCC-specific mortality rate; second, in matched analyses RN predisposes to 1.5-fold higher RCC-specific mortality rate; and third, both analyses failed to demonstrate statistically significant differences. Based on these findings it could be postulated that until further data become available, NSS does not appear to undermine RCC-specific survival in carefully selected patients with MRCC. The power analyses demonstrated that at least 146, 48, and 76 observations per arm are necessary at 1, 2, and 3 years, respectively, to confirm survival equivalence. CONCLUSIONS: Although the data were limited in size and completeness, they may indicate that RCC-specific survival may not be undermined if NSS is performed in properly selected cases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".