1290 DOES SURGEON AND HOSPITAL VOLUME AFFECT OUTCOMES FOR SURGERY FOR RENAL CELL CARCINOMA WITH INFERIOR VENA CAVA INVOLVEMENT? – RESULTS OF A NATIONAL POPULATION BASED STUDY
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Résumé
You have accessJournal of UrologyKidney Cancer: Localized II1 Apr 20121290 DOES SURGEON AND HOSPITAL VOLUME AFFECT OUTCOMES FOR SURGERY FOR RENAL CELL CARCINOMA WITH INFERIOR VENA CAVA INVOLVEMENT? – RESULTS OF A NATIONAL POPULATION BASED STUDY Paul Toren, Robert Abouassaly, Narhari Timilshina, Girish Kulkarni, and Antonio Finelli Paul TorenPaul Toren Toronto, Canada More articles by this author , Robert AbouassalyRobert Abouassaly Cleveland, OH More articles by this author , Narhari TimilshinaNarhari Timilshina Toronto, Canada More articles by this author , Girish KulkarniGirish Kulkarni Toronto, Canada More articles by this author , and Antonio FinelliAntonio Finelli Toronto, Canada More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2012.02.1624AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES In several major surgical procedures, an association with provider volume and outcomes has been seen, justifying a centralization of these procedures. Radical nephrectomy with removal of inferior vena cava (IVC) thrombus is a relatively rare, but large and complex operation in urology. Using Canada-wide population based data, we determined to assess whether surgeon or hospital volume had an effect on in-hospital mortality and complications. METHODS The Canadian Institute for Health Information(CIHI) Canadian Classification of Health Intervention(CCI) codes and Canadian Classification of Diagnostic, Therapeutic, and Surgical Procedures (CCP) codes were used to identify all nephrectomies associated with IVC thrombus performed in 9/10 Canadian provinces from 1998-2007. The CIHI Discharge Abstract Database (DAD) was used to assess in-hospital mortality and surgical complication rates for each procedure. The Charlson Co-morbidity Index (CCI) for each patient was calculated from ICD-9 and ICD-10 codes. Patients were excluded who underwent a partial nephrectomy, laparoscopic nephrectomy or had incomplete data. RESULTS During the study period, 816 nephrectomies associated with venous thrombus were performed on 521 men and 295 women. The in-hospital mortality rate was 7% (59 patients); surgical complications were noted in 122 (15%) of patients. Age and co-morbidity were the strongest predictors of in hospital mortality on multivariate logistic analysis. Multivariate logistic regression analysis showed a trend to lower in-hospital mortality with higher surgeon volume which was significant at the highest quartile (OR for highest vs lowest quartile 0.42(0.0.18-0.98; P=0.05)). This relationship was not seen with hospital volume (P= 0.34). Over time, more surgeries were performed by the higher quartile surgeons. Most (65%) surgical complications were split between the highest and lowest quartiles of surgeon volume. With increasing hospital volume, there was a trend for increased complications on multivariate analysis (OR 2.1 (2.1-4.1; P=0.03). CONCLUSIONS For radical nephrectomies associated with IVC thrombus, increasing surgeon volume, but not hospital volume, corresponded to lower in-hospital mortality rates. Age and co-morbidity remain the strongest predictors of in-hospital mortality. Higher hospital volume quartile was associated with higher surgical complications. © 2012 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 187Issue 4SApril 2012Page: e523 Advertisement Copyright & Permissions© 2012 by American Urological Association Education and Research, Inc.MetricsAuthor Information Paul Toren Toronto, Canada More articles by this author Robert Abouassaly Cleveland, OH More articles by this author Narhari Timilshina Toronto, Canada More articles by this author Girish Kulkarni Toronto, Canada More articles by this author Antonio Finelli Toronto, Canada 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 | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».