Postoperative Mortality Rate after Radical Cystectomy: A Systematic Review of Epidemiologic Series
Bibliographic record
Abstract
INTRODUCTION: Mortality after radical cystectomy (RC) varies widely in the literature. In cohort studies, mortality rates can vary from as low as 0.5% in large-volume academic centers (2) to as high as 25% in developing countries series. This study aims to perform a systematic review of population-based studies reporting mortality after RC. METHODS: A Systematic search was performed in Medline (PubMed®), Embase, and Cochrane for epidemiologic studies reporting mortality after RC. Institutional cohorts and those reporting mortality for specific groups within populations were excluded. Case series and non-epidemiologic series were also excluded. The aim of this review is to evaluate in-hospital mortality (IHM), 30-day mortality (30M), and 90-day mortality (90M). RESULTS: Systematic search resulted in 42 papers comprising 449,661 patients who underwent RC from 1984 to 2017. Mean age was 66.1. Overall IHM, 30M, and 90M were 2.6%, 2.7%, and 4.9%, respectively, with 90M being 2.6 times higher than IHM on average. Lowest IHM was found in Canada and Australia (0.2% and 0.6%, respectively), while the highest IHM was 7.8% (Brazil). Canada and Spain showed the highest 90M (6.5%). 159,584 urinary diversions were analyzed, being mostly ileal conduits (76.8%). CONCLUSIONS: The majority of the studies available are from major developed economies with paucity of data in the developing world. 90M after RC tends to be at least twice as high as IHM. The knowledge of such epidemiologic data is vital to guide public policies, such as centralization, in order to reduce mortality.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".