1027 A POPULATION-BASED COMPETING-RISKS ANALYSIS OF THE SURVIVAL OF PATIENTS TREATED WITH RADICAL CYSTECTOMY FOR BLADDER CANCER
Bibliographic record
Abstract
You have accessJournal of UrologyBladder Cancer: Staging1 Apr 20101027 A POPULATION-BASED COMPETING-RISKS ANALYSIS OF THE SURVIVAL OF PATIENTS TREATED WITH RADICAL CYSTECTOMY FOR BLADDER CANCER Giovanni Lughezzani, Claudio Jeldres, Lars Budaus, Maxine Sun, Rodolphe Thuret, Shahrokh F. Shariat, Daniel Liberman, Hugues Widmer, Philippe Arjane, Markus Graefen, Seth P. Lerner, Paul Perrotte, Francesco Montorsi, and Pierre I. Karakiewicz Giovanni LughezzaniGiovanni Lughezzani Milano, Italy More articles by this author , Claudio JeldresClaudio Jeldres Montreal, Canada More articles by this author , Lars BudausLars Budaus Hamburg, Germany More articles by this author , Maxine SunMaxine Sun Montreal, Canada More articles by this author , Rodolphe ThuretRodolphe Thuret Montpellier, France More articles by this author , Shahrokh F. ShariatShahrokh F. Shariat Montreal, Canada More articles by this author , Daniel LibermanDaniel Liberman Montreal, Canada More articles by this author , Hugues WidmerHugues Widmer Montreal, Canada More articles by this author , Philippe ArjanePhilippe Arjane Montreal, Canada More articles by this author , Markus GraefenMarkus Graefen Hamburg, Germany More articles by this author , Seth P. LernerSeth P. Lerner Houston, TX More articles by this author , Paul PerrottePaul Perrotte Montreal, Canada More articles by this author , Francesco MontorsiFrancesco Montorsi Milano, Italy More articles by this author , and Pierre I. KarakiewiczPierre I. Karakiewicz Montreal, Canada More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2010.02.2062AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The radical cystectomy (RC) population is very heterogeneous with respect of cancer-specific (CSM) and other-cause mortality (OCM). Comorbidities and comorbidities-associated events represent very important causes of mortality in RC patients. We examined the rates of CSM and OCM in a population-based RC cohort. METHODS We identified 11260 patients treated with RC for urothelial carcinoma of the urinary bladder between 1988 and 2006 within 17 Surveillance, Epidemiology and End Results database. Patients were stratified into 20 age-tumor stage categories. Poisson regression models were fitted to obtain estimates of CSM and OCM mortality rates at specific time points after RC. RESULTS After stratification according to disease stage and patient age, CSM resulted as the main cause of mortality in all patients strata, Nonetheless, at 5 years after RC between 8.5 and 27.1% of deaths were attributable to OCM. The three most common causes of OCM were heart disease, other malignancies and chronic obstructive pulmonary disease. The most prominent effect on CSM was exerted by locally-advanced BCa stages. Conversely, age was the main determinant of OCM. Interestingly, even after adjusting for bladder cancer pathologic stage, CSM was higher in older individuals than in younger patients. CONCLUSIONS Our model represents a valuable tool capable of predicting OCM and CSM according to disease stage and patient age. The model results can help clinicians to better stratify the risk-benefit ratio of RC. Hopefully, this tool will be integrated into patient counseling and informed consent process prior to RC in patients who represent surgical candidates. © 2010 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 183Issue 4SApril 2010Page: e399 Advertisement Copyright & Permissions© 2010 by American Urological Association Education and Research, Inc.MetricsAuthor Information Giovanni Lughezzani Milano, Italy More articles by this author Claudio Jeldres Montreal, Canada More articles by this author Lars Budaus Hamburg, Germany More articles by this author Maxine Sun Montreal, Canada More articles by this author Rodolphe Thuret Montpellier, France More articles by this author Shahrokh F. Shariat Montreal, Canada More articles by this author Daniel Liberman Montreal, Canada More articles by this author Hugues Widmer Montreal, Canada More articles by this author Philippe Arjane Montreal, Canada More articles by this author Markus Graefen Hamburg, Germany More articles by this author Seth P. Lerner Houston, TX More articles by this author Paul Perrotte Montreal, Canada More articles by this author Francesco Montorsi Milano, Italy More articles by this author Pierre I. Karakiewicz Montreal, Canada More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".