Temporal trends and clinical determinants of urinary diversion after radical cystectomy
Bibliographic record
Abstract
OBJECTIVES: To evaluate the temporal trends in types of urinary diversion (UD) used after radical cystectomy (RC) in a large, multicentre, international cohort over the past two decades. MATERIALS AND METHODS: We analysed 6469 patients who underwent RC between 2004 and 2024 at 23 international tertiary referral centres. Trends in UD type (cutaneous ureterostomy [UCS], ileal conduit [IC], and neobladder) were assessed using estimated annual percentage change (EAPC). Multivariable analysis (MVA) models identified preoperative predictors of UD type. EAPC was applied to evaluate temporal changes in the patient characteristics associated with UD type. RESULTS: Overall, 882 (14%), 3611 (56%) and 1976 patients (31%) underwent UCS, IC, and neobladder procedures, respectively. IC remained the most common UD, without significant temporal change (P = 0.1). UCS use increased from 2% to 22% (EAPC 9.9%; P < 0.001), while neobladder use declined from 41% to 19% (EAPC -2%; P = 0.009). MVA showed that older age, comorbidities, and advanced disease were associated with higher rates of UCS and lower rates of neobladder use (all P < 0.005). Neoadjuvant chemotherapy (NAC) was inversely linked to UCS, while robot-assisted RC and male sex favoured neobladder use (all P < 0.005). EAPC showed rising proportions of male patients (EAPC 6.7%), patients aged >70 years (1.2%), patients with a Charlson Comorbidity Index ≥3 (8.3%), patients who received NAC (10.4%) and patients with cT2-cN0 disease (0.5%; all P < 0.05). CONCLUSION: Over two decades, a marked increase in UCS use has been observed, alongside a decline in neobladder reconstruction. These trends coincided with a shift toward older, more comorbid patients undergoing RC. Evolving patient profiles and surgical practices underscore the need for tailored UD strategies and optimised peri-operative management.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".