1591 AGE, COMORBIDITIES, AND RACE ARE PREDICTORS TO UNDERGO RADICAL CYSTECTOMY AT LOW VOLUME INSTITUTIONS
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Résumé
You have accessJournal of UrologyBladder Cancer: Invasive II1 Apr 20121591 AGE, COMORBIDITIES, AND RACE ARE PREDICTORS TO UNDERGO RADICAL CYSTECTOMY AT LOW VOLUME INSTITUTIONS Marco Bianchi, Maxine Sun, Jens Hansen, Nawar Hanna, Zhe Tian, Alberto Briganti, Shahrokh Shariat, Paul Perrotte, Francesco Montorsi, and Pierre Karakiewicz Marco BianchiMarco Bianchi Milan, Italy More articles by this author , Maxine SunMaxine Sun Montreal, Canada More articles by this author , Jens HansenJens Hansen Hamburg, Germany More articles by this author , Nawar HannaNawar Hanna Montreal, Canada More articles by this author , Zhe TianZhe Tian Montreal, Canada More articles by this author , Alberto BrigantiAlberto Briganti Milan, Italy More articles by this author , Shahrokh ShariatShahrokh Shariat New York, NY More articles by this author , Paul PerrottePaul Perrotte Montreal, Canada More articles by this author , Francesco MontorsiFrancesco Montorsi Milan, Italy More articles by this author , and Pierre KarakiewiczPierre Karakiewicz Montreal, Canada More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2012.02.1364AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES We tested the hypothesis that old age, multiple comorbidities, and race may predict radical cystectomy at low volume institutions. METHODS Overall, 10991 patients treated with radical cystectomy for bladder cancer were identified amongst 1052 hospitals originating from the Nationwide Inpatient Sample, between years 1998 and 2007. We examined patient age, baseline Charlson comorbidity index (CCI), gender, race, hospital teaching status, hospital region, and annual household income according to hospital volume, which was modeled in a continuously coded fashion. Finally, we examined the effect of hospital volume on patient age and CCI, using linear regression analyses. Adjustment was made for all the aforementioned covariates. RESULTS The overall mean hospital volume was 8 cystectomies per year (median 4, interquartile range [IQR]: 2–8). First, hospital volume decreased with increasing age (≤59 years mean: 8.6 (median 4) vs. ≥80 years: 7.6 (median 4), P<0.001) and increasing CCI (0 mean: 8.6 (median 4) vs. ≥3: 6 (median 3), P<0.001). The effect of hospital volume also differed according to gender, hospital teaching status, and hospital region. Specifically, females, patients of black race, non-teaching hospitals, and hospitals located in the Midwest, were treated at institutions with the lowest hospital volume. In univariable linear regression analyses, decreasing age (beta: -0.038, P<0.001) and decreasing CCI (beta: -0.039, P<0.001) were inversely associated with increasing hospital volume. These findings were confirmed in multivariable analyses, where patients with increasing age (beta: -0.022, P=0.03) and higher CCI (beta: -0.028, P=0.005) were more likely to be operated at hospitals with a low hospital volume. CONCLUSIONS Our data show that advanced age, multiple comorbidities, black race, and female gender are predictor of radical cystectomy at low volume institution. Clustering of patients with those characteristics at low volume institutions does not appear to be incidental and may predispose to worse outcomes © 2012 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 187Issue 4SApril 2012Page: e644 Advertisement Copyright & Permissions© 2012 by American Urological Association Education and Research, Inc.MetricsAuthor Information Marco Bianchi Milan, Italy More articles by this author Maxine Sun Montreal, Canada More articles by this author Jens Hansen Hamburg, Germany More articles by this author Nawar Hanna Montreal, Canada More articles by this author Zhe Tian Montreal, Canada More articles by this author Alberto Briganti Milan, Italy More articles by this author Shahrokh Shariat New York, NY More articles by this author Paul Perrotte Montreal, Canada More articles by this author Francesco Montorsi Milan, Italy More articles by this author Pierre Karakiewicz Montreal, 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 | 0,000 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,078 | 0,010 |
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 ».