113 IMPACT OF HOSPITAL AND SURGEONS VOLUME ON COMPLICATION RATES AFTER RADICAL CYSTECTOMY: POPULATION BASED STUDY
Bibliographic record
Abstract
You have accessJournal of UrologyGeneral & Epidemiological Trends & Socioeconomics: Evidence-based Medicine & Outcomes III1 Apr 2010113 IMPACT OF HOSPITAL AND SURGEONS VOLUME ON COMPLICATION RATES AFTER RADICAL CYSTECTOMY: POPULATION BASED STUDY Lars Budäus, Giovanni Lughezzani, Maxine Sun, Rodolphe Thuret, Hendrik Isbarn, Felix Chun, Sascha Ahyai, Roland Dahlem, Paul Perrotte, Hugues Widmer, Philippe Arjane, Francesco Montorsi, Shahrokh F. Shariat, Margit Fisch, Markus Graefen, and Pierre I. Karakiewicz Lars BudäusLars Budäus Hamburg, Germany More articles by this author , Giovanni LughezzaniGiovanni Lughezzani Milano, Italy More articles by this author , Maxine SunMaxine Sun Montreal, Canada More articles by this author , Rodolphe ThuretRodolphe Thuret Montreal, Canada More articles by this author , Hendrik IsbarnHendrik Isbarn Hamburg, Germany More articles by this author , Felix ChunFelix Chun Hamburg, Germany More articles by this author , Sascha AhyaiSascha Ahyai Hamburg, Germany More articles by this author , Roland DahlemRoland Dahlem Hamburg, Germany More articles by this author , Paul PerrottePaul Perrotte 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 , Francesco MontorsiFrancesco Montorsi Milano, Italy More articles by this author , Shahrokh F. ShariatShahrokh F. Shariat Montreal, Canada More articles by this author , Margit FischMargit Fisch Hamburg, Germany More articles by this author , Markus GraefenMarkus Graefen Hamburg, Germany 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.2010.02.163AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The hypothesis that practice makes perfect has been previously examined in different types of urologic cancer surgery. We tested the hypothesis, that surgical volume (SV) and hospital volume (HV) predict complication rates after radical cystectomy for urothelial cancer of urinary bladder. METHODS Between 2003 and 2008 in the state of Florida, 2719 patients underwent a RC for urothelial carcinoma of the bladder. All complications that occurred during hospital stay were recorded in the Florida inpatient database and classified into six different categories according to an established scheme. The effect of surgical volume (SV) and hospital volume (HV), both divided into tertiles, was then tested in univariable and multivariable logistic regression models. The 90% percentile was used as a further reference group. Covariates consisted of age, race, gender and comorbidities. RESULTS Overall complications occurred in 1043 patients (38.4%). After stratification according to complication type, the following rates were recorded 2.2%; 2.4%; 2.4%; 4.0%; 7.3%; 2.6%; 0.7% 0.8%; 2,5%; 0.8%; 3.2% for vascular, respiratory, urinary, cardiac, infection, hemorrhage and hematoma and wound infection. Analyses that focused on the effect of SV on recorded complication rates showed that low volume surgeons have higher complications compared to intermediate and high volume surgeons. Similarly hospitals with low volume had the highest complication rate vs. intermediate vs. high volume hospitals. In multivariate analysis SV and HV categories (low SV vs. intermediate SV O.R. 1.42 p<0.013; low SV vs. high SV 1.47 p<0.009) and (low HV vs. intermediate HV O.R. 1.81 p<0.0001; low HV vs. high HV 1.83 p<0.0001) categories represented independent predictors of overall complication rate. A high surgical volume exerted a protective effect on the recorded rates, even after adjustment for covariates and hospital volume. CONCLUSIONS High SV and high HV exert a protective effect on inhospital complication rates after radical cystectomy, even after adjustment for covariates like age, gender and comorbidities. This consideration should be taken into account when best therapeutic options are considered. © 2010 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 183Issue 4SApril 2010Page: e46-e47 Advertisement Copyright & Permissions© 2010 by American Urological Association Education and Research, Inc.MetricsAuthor Information Lars Budäus Hamburg, Germany More articles by this author Giovanni Lughezzani Milano, Italy More articles by this author Maxine Sun Montreal, Canada More articles by this author Rodolphe Thuret Montreal, Canada More articles by this author Hendrik Isbarn Hamburg, Germany More articles by this author Felix Chun Hamburg, Germany More articles by this author Sascha Ahyai Hamburg, Germany More articles by this author Roland Dahlem Hamburg, Germany More articles by this author Paul Perrotte 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 Francesco Montorsi Milano, Italy More articles by this author Shahrokh F. Shariat Montreal, Canada More articles by this author Margit Fisch Hamburg, Germany More articles by this author Markus Graefen Hamburg, Germany 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".