Nephrectomy improves the survival of patients with locally advanced renal cell carcinoma
Bibliographic record
Abstract
OBJECTIVES: To examine the cancer-specific survival of patients treated with nephrectomy and compared it to that of patients managed without surgery. PATIENTS AND METHODS: Of 43,143 patients with renal cell carcinoma (RCC) identified in the 1988-2004 Surveillance, Epidemiology and End Results database, 7068 had locally advanced RCC and with no distant metastasis. These patients had a nephrectomy (6786, 96.0%) or no surgical therapy (282, 4.0%). Multivariable Cox regression models, and matched and unmatched Kaplan-Meier survival analyses, were used to compare the effect of nephrectomy vs non-surgical therapy on cancer-specific survival. Also, competing-risks regression models adjusted for the effect of other-cause mortality. Covariates and matching variables consisted of age, gender, tumour size and year of diagnosis. RESULTS: The 1-, 2-, 5- and 10-year cancer-specific survival of patients who had nephrectomy was 88.9%, 88.1%, 68.6% and 57.5%, vs 44.8%, 30.6%, 14.5% and 10.6% for non-surgical therapy. In multivariable analyses, relative to nephrectomy, non-surgical therapy was associated with a 5.8-fold higher rate of cancer-specific mortality (P < 0.001). Non-surgical therapy was also associated with a 5.1-fold higher rate of cancer-specific mortality in matched analyses (P < 0.001). Finally, competing-risks regression confirmed the statistical significance of the variable defining treatment type (nephrectomy vs non-surgical therapy) in multivariable and matched analyses (P < 0.001). CONCLUSION: Relative to non-surgical treatment, nephrectomy improves the cancer-specific survival of patients with locally advanced RCC; our findings await prospective confirmation.
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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 | 0.000 | 0.001 |
| 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".