Partial nephrectomy for the treatment of renal cell carcinoma ( <scp>RCC</scp> ) and the risk of end‐stage renal disease ( <scp>ESRD</scp> )
Bibliographic record
Abstract
OBJECTIVE: To assess whether radical nephrectomy (RN) compared with partial nephrectomy (PN) for the treatment of renal cell carcinoma (RCC) is associated with greater risk of end-stage renal disease (ESRD). PATIENTS AND METHODS: We performed a population-based, retrospective cohort study using linked administrative databases in the province of Ontario, Canada. We included individuals with pathologically confirmed RCC diagnosed between 1995 and 2010. Cox proportional hazards, propensity score, and competing risks models were used to assess the impact of treatment choice. The primary outcome was ESRD. Secondary outcomes included overall mortality, myocardial infarction, and new-onset chronic kidney disease (CKD). A modern cohort of patients (2003-2010) was analysed separately. RESULTS: We included 11,937 patients, of whom 2107 (18%) underwent PN. The median follow-up was 57 months. In the full cohort, type of surgery was not associated with the rate of ESRD, whereas PN was associated with a decreased likelihood of ESRD compared with RN in the modern cohort using a multivariable proportional hazards model [hazard ratio (HR) 0.44, 95% confidence interval (CI) 0.25-0.75) or propensity score modelling (HR 0.48, 95% CI 0.27-0.82). PN was also associated with a lower risk of new-onset CKD (HR 0.48, 95% CI 0.41-0.57). CONCLUSIONS: Although it is well-known that RN is associated with more CKD than PN, we provide the first direct evidence that PN is associated with less ESRD requiring renal replacement therapy than RN in a modern cohort of patients with RCC.
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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 | 0.002 |
| 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".