Survival Outcomes in Bladder Cancer: An Ambispective Study of Patients Undergoing Radical Cystectomy
Bibliographic record
Abstract
Introduction Radical cystectomy (RC) with pelvic lymph node dissection and urinary diversion is the preferred treatment for non-metastatic muscle invasive bladder cancer (MIBC) and for some high-risk non-muscle invasive bladder cancer (NMIBC), in patients fit for major surgery. Primary objective The primary objective of this study is to estimate the overall survival (OS) of patients with MIBC who have undergone RC. Secondary objective The secondary objectives are to estimate the disease-free survival (DFS) of patients with MIBC following RC and to determine factors that influence OS and DFS in this population. Methodology This was an ambispective study of patients who underwent RC for carcinoma of the urinary bladder between 1st January 2010 and 30th April 2022. Clinicopathological data were obtained from the patients' clinical records. Results The study included a total of 106 patients, with a mean age of 59.35 years. Of these, 55.88% had received neoadjuvant chemotherapy (NACT), and 13 patients (11.7%) underwent salvage cystectomy. Only five patients received intravesical chemotherapy instillation following transurethral resection of bladder tumor (TURBT). RC was performed via laparotomy in 82 patients, while 24 patients underwent a laparoscopic approach. The mean hospital stay was five days. Ninety-eight patients had Clavien-Dindo grade II morbidity. There were no intra-hospital or 30-day mortalities. The histopathologic report showed that 21 patients had achieved pathological complete response (PCR). A total of 96 patients were kept on follow-up, of whom 32 patients had recurrence. Median DFS was 145 months, and median OS was 118 months. PCR was found to be significantly associated with improved DFS. Smoking was found to be a significant predictor of OS on multivariate analysis. Conclusions Although NACT did not reach statistical significance as an independent predictor of survival, it contributed to the achievement of PCR, which was predictive of DFS and OS. Hence, NACT should be pursued wherever feasible. Further, this study shows the importance of smoking cessation, as current or recent smoking was found to have a negative impact on survival among patients with bladder cancer.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".