Kidney disease progression in patients of upper tract urothelial carcinoma following unilateral radical nephroureterectomy
Bibliographic record
Abstract
OBJECTIVES: To compare the renal outcomes in patients of unilateral renal cell carcinoma (RCC) with upper tract urothelial carcinoma (UTUC) following surgical resection of the tumor-bearing kidney, and to investigate the potential predictors in renal function decline. PATIENTS AND METHODS: In this retrospective cohort study, 319 RCC patients undergoing radical nephrectomy (RN) and 297 UTUC patients undergoing radical nephroureterectomy were recruited from a tertiary medical center between 2001 and 2010. Demographic data, co-morbidity, smoking habit, baseline estimated glomerular filtration rate (eGFR) calculated by chronic kidney disease-epidemiology equation, as well as tumor staging of RCC and UTUC, were recorded. The primary endpoint was serum creatinine doubling and/or end-stage renal disease (ESRD) necessitating long-term dialysis. Cox proportional hazard model and Fine and Gray's competing risk regression accounting for death were used to model renal outcome. RESULTS: UTUC patients had a higher incidence rate of renal function deterioration than RCC patients did (15.01 vs. 2.68 per 100 person-years, p<0.001). In Cox proportional hazard model and Fine and Gray's competing risk regression, UTUC was significantly associated with increased risk of creatinine doubling and/or ESRD necessitating dialysis (hazard ratio, 3.13; 95% confidence interval, 2.01-4.87) as compared to RCC following unilateral RN. Nevertheless, our study is observational in nature and cannot prove causality. CONCLUSIONS: UTUC per se is strongly associated with kidney disease progression as compared to RCC following unilateral nephrectomy. Further studies are needed to elucidate this association.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".