Predictors of long-term renal function after kidney surgery for patients with preoperative chronic kidney disease
Bibliographic record
Abstract
INTRODUCTION: We evaluated the trajectory of estimated glomerular filtration rate (eGFR) after kidney surgery in patients with kidney cancer and chronic kidney disease (CKD). METHODS: We identified 1204 consecutive patients in our institutional database with preoperative CKD undergoing partial or radical nephrectomy from 1998-2016. Postoperative eGFR was tracked, with patients censored when receiving dialysis or kidney transplantation. A multivariable mixed-effects models assessed associations between preoperative baseline patient and tumor characteristics, and longitudinal eGFR. The Kaplan-Meier method and multivariable Cox regression were used to estimate overall survival, cancer-specific survival, and cumulative incidence of dialysis. RESULTS: Preoperatively, 892 (74.1%), 271 (22.5%), and 41 (3.4%) patients had CKD stage 3a, 3b, and 4/5, respectively. There were 55 patients dialyzed and 355 deaths (99 from kidney cancer). Median followup was 8.1 years, with 25 781 postoperative eGFR measurements. Factors associated with decreasing eGFR postoperatively included radical nephrectomy, male gender, older age, increased body mass index (BMI), and cardiovascular risk factors. We observed a significant interaction effect between time from surgery and preoperative CKD stage: the eGFR of stage 3a patients improved, while stage ≥3b declined (p<0.001). The two-year and five-year cumulative incidence of dialysis was 1.8% (1.1-2.6%) and 3.1% (2.2-4.2%), respectively. The cumulative incidence of dialysis, with death as a competing event, significantly differed by preoperative CKD stage. CONCLUSIONS: Preoperative CKD stage ≥3b is independently associated with a higher risk of declining renal function, dialysis, and mortality. With careful selection, patients with preoperative CKD withstand kidney surgery with low rates of dialysis.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".