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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".