Chronic kidney disease as a risk factor for recurrence and progression in patients with primary non‐muscle‐invasive bladder cancer
Bibliographic record
Abstract
OBJECTIVES: To investigate the relationship between chronic kidney disease and primary non-muscle-invasive bladder cancer. METHODS: Disease outcomes were analyzed in 418 patients treated with transurethral resection for primary non-muscle-invasive bladder cancer, and were correlated to traditional risk factors as well as chronic kidney disease stage according to estimated glomerular filtration rate: ≥60 (G1-2), 45-59 (G3a) or <45 (G3b-5). RESULTS: The median follow-up time was 40.0 months. There were 287 (68.7%), 98 (23.4%), and 33 (7.9%) patients with G1-2, G3a and G3b-5 chronic kidney disease, respectively. T1 tumor was present in 29.6% of G1-2, 43.9% of G3a and 51.4% of G3b-5 chronic kidney disease (P = 0.004). The proportion of histological grade 3 non-muscle-invasive bladder cancer was higher in G3a and G3b-5 than G1-2 (P < 0.001). Higher chronic kidney disease stage was associated with worse recurrence-free (P < 0.001) and progression-free survival (P = 0.017). In multivariable analysis, G3b-5 was found to be an independent predictor for recurrence (hazard ratio 1.87; P = 0.004) and progression (hazard ratio 2.96; P = 0.019). Chronic kidney disease stage was also strongly associated with the European Association of Urology bladder cancer risk groups (P < 0.001), and with shorter time to recurrence and progression in each group. CONCLUSIONS: Chronic kidney disease predicts the clinical outcome of primary non-muscle-invasive bladder cancer. Adding chronic kidney disease to the conventional risk factors might increase the accuracy of risk stratification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".