Long-term outcomes after radical or partial nephrectomy for T1a renal cell carcinoma: A population-based study
Bibliographic record
Abstract
INTRODUCTION: The benefit of partial nephrectomy (PN) compared to radical nephrectomy (RN) for T1a renal cell carcinoma (RCC) remains uncertain, with observational studies conflicting with level 1 evidence. Therefore, the purpose of this population-based study was to compare long-term outcomes in patients undergoing PN or RN for T1a RCC. METHODS: We studied 5670 patients in Ontario, Canada undergoing PN or RN for T1a RCC. The primary outcome was overall survival (OS). Secondary outcomes were cancer-specific survival (CSS), chronic kidney disease (CKD), renal replacement therapy, and myocardial infarction (MI). We used multivariable Cox proportional hazard models to evaluate the association between PN or RN and these outcomes. A sensitivity analysis was performed in patients with a preoperative serum creatinine available. RESULTS: Median followup was 77 months. Compared to RN, PN was associated with significantly improved OS (hazard ratio [HR] 0.73, 95% confidence interval [CI] 0.63-0.84), reduced risk of CKD (HR 0.18, 95% CI 0.12-0.27), and improved CSS (HR 0.45, 95% CI 0.30-0.65). The risk of MI was not significantly different between groups (HR 0.91, 95% CI 0.62-1.34). Few patients (n=15) required renal replacement therapy. In the sensitivity analysis, the association between type of surgery and OS and CKD persisted, while the association with CSS did not. CONCLUSIONS: Our study found that in patients undergoing surgery for T1a RCC, PN was associated with improved OS and reduced risk of CKD compared to RN. However, few patients in either group required renal replacement therapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".