Factors predicting early mortality after radical cystectomy for urothelial carcinoma in a contemporary cohort of patients
Bibliographic record
Abstract
INTRODUCTION: We aimed to identify preoperatively available patient variables associated with increased mortality within 30 and 90 days of radical cystectomy (RC) for localized urothelial carcinoma (UC), and to evaluate temporal trends in early mortality rates. METHODS: We reviewed the National Cancer Database to identify patients who underwent RC for UC between 2006 and 2013. Preoperatively available patient-specific demographics and mortality rates at 30 and 90 days postoperatively were analyzed. Univariable and multivariable logistic regression analyses were performed to examine factors associated with 30- and 90-day mortality. RESULTS: We identified 37 366 patients who underwent RC between 2006 and 2013. Overall mortality rates remained stable over time. From 2006-2013, 936 patients (2.5%) and 2554 patients (6.8%) died of any cause within 30 and 90 days post-RC, respectively. On multivariable analysis, increased age, higher clinical T and N stage, increased Charlson-Deyo comorbidity classification, African-American race, lower hospital volume, non-academic centers, lower patient income, and absence of insurance were each significantly associated with increased early mortality after RC (p<0.05). The protective effect of higher hospital volume was similar regardless of patient's age, clinical stage, or comorbidity status. CONCLUSIONS: Our study identified patient-specific variables that are significantly associated with increased early mortality after RC. These findings can be used in counselling to identify ideal candidates for RC to decrease patient harm. Furthermore, early mortality rates after RC have remained stable over time, indicating that ongoing quality improvement is essential to improve outcomes.
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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 | 0.000 |
| 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".