PD57-08 CENTRALIZATION OF RADICAL CYSTECTOMY FOR BLADDER CANCER IN A UNIVERSAL HEALTHCARE SYSTEM: EARLY RESULTS FROM A CANADIAN ACADEMIC CENTER
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Résumé
You have accessJournal of UrologyBladder Cancer: Epidemiology & Evaluation III1 Apr 2017PD57-08 CENTRALIZATION OF RADICAL CYSTECTOMY FOR BLADDER CANCER IN A UNIVERSAL HEALTHCARE SYSTEM: EARLY RESULTS FROM A CANADIAN ACADEMIC CENTER Jan Rudzinski, Niels Jacobsen, Eric Estey, Sunita Ghosh, Scott North, Naveen Basappa, Michael Kolinsky, and Adrian Fairey Jan RudzinskiJan Rudzinski More articles by this author , Niels JacobsenNiels Jacobsen More articles by this author , Eric EsteyEric Estey More articles by this author , Sunita GhoshSunita Ghosh More articles by this author , Scott NorthScott North More articles by this author , Naveen BasappaNaveen Basappa More articles by this author , Michael KolinskyMichael Kolinsky More articles by this author , and Adrian FaireyAdrian Fairey More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2017.02.2610AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Radical cystectomy for bladder cancer is a complex surgical oncology procedure. Accumulating data suggest variation in outcomes based on hospital and surgeon characteristics. Centralization of this procedure to high volume, fellowship-trained surgeons may improve clinical outcomes. High quality data examining the impact of radical cystectomy centralization are lacking. At the University of Alberta, radical cystectomy was centralized at a single institution and performed by 1 of 2 urologic oncologists starting in August 2013. Our objective was to compare outcomes of radical cystectomy before and after centralization of care. METHODS A retrospective analysis of data from the University of Alberta Radical Cystectomy Database was performed. Eligible subjects were those with histologically proven urothelial carcinoma of the bladder (cTanyN1-3M0) undergoing curative intent surgery. Patients were classified into pre-centralization era (1994-2007; N=523) and post-centralization era (2013-present; N=134) cohorts for analyses. Pre-centralization era patients were treated by 1 of 11 urologic surgeons at 2 academic teaching hospitals. Post-centralization era patients were treated by 1 of 2 fellowship-trained urologic oncologists at 1 academic teaching hospital. Outcomes were overall survival, 90-day mortality rate, positive surgical margin (R1) resection rate, total number of lymph nodes evaluated, and 90-day blood product transfusion rate. The Kaplan-Meier method and multivariable regression analyses were used to analyze survival outcomes. Statistical tests were two-sided (p≤0.05). RESULTS The median follow-up duration in the pre- and post-centralization era was 33 months and 16 months, respectively. The predicted 2-year overall survival rate was 62% in the pre-centralization era and 84% in the post-centralization era (Log rank P=0.0007; multivariable HR 0.40, 95% CI 0.24 to 0.68, P<0.0001). Treatment in the post-centralization era was associated with lower 90-day mortality (6.3% versus 1.5%, multivariable OR 0.23, 95% CI 0.06 to 0.99, P=0.049), R1 resection (13.0% versus 1.5%; multivariable OR 0.07, 95% CI 0.01 to 0.51, P=0.009), and 90-day blood product transfusion (59% versus 6%, P<0.0001) as well as higher total number of lymph nodes evaluated (7 versus 30 lymph nodes, P<0.0001). CONCLUSIONS Surgical treatment in the post-centralization era was associated with superior survival, cancer control, and perioperative outcomes. © 2017FiguresReferencesRelatedDetails Volume 197Issue 4SApril 2017Page: e1124 Advertisement Copyright & Permissions© 2017MetricsAuthor Information Jan Rudzinski More articles by this author Niels Jacobsen More articles by this author Eric Estey More articles by this author Sunita Ghosh More articles by this author Scott North More articles by this author Naveen Basappa More articles by this author Michael Kolinsky More articles by this author Adrian Fairey More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,047 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,011 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,005 | 0,002 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».