1293 ACTIVE SURVEILLANCE MAY INCREASE THE RISK OF CANCER-SPECIFIC MORTALITY RELATIVE TO PARTIAL OR RADICAL NEPHRECTOMY: A COMPETING-RISKS ANALYSIS
Notice bibliographique
Résumé
You have accessJournal of UrologyKidney Cancer: Localized II1 Apr 20121293 ACTIVE SURVEILLANCE MAY INCREASE THE RISK OF CANCER-SPECIFIC MORTALITY RELATIVE TO PARTIAL OR RADICAL NEPHRECTOMY: A COMPETING-RISKS ANALYSIS Maxine Sun, Marco Bianchi, Jens Hansen, Quoc-Dien Trinh, Nawar Hanna, Markus Graefen, Francesco Montorsi, Paul Perrotte, and Pierre Karakiewicz Maxine SunMaxine Sun Montreal, Canada More articles by this author , Marco BianchiMarco Bianchi Milan, Italy More articles by this author , Jens HansenJens Hansen Hamburg, Germany More articles by this author , Quoc-Dien TrinhQuoc-Dien Trinh Detroit, MI More articles by this author , Nawar HannaNawar Hanna Montreal, Canada More articles by this author , Markus GraefenMarkus Graefen Hamburg, Germany More articles by this author , Francesco MontorsiFrancesco Montorsi Milan, Italy More articles by this author , Paul PerrottePaul Perrotte Montreal, Canada 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.2012.02.1627AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The current American Urological Association guidelines recommend active surveillance (AS) in selected patients for the management of small renal masses. We sought to assess and compare survival between surgical intervention relative to AS. METHODS Using the Surveillance, Epidemiology, and End Results database, patients with T1aN0M0 renal cell carcinoma (RCC), treated with partial nephrectomy (PN), radical nephrectomy (RN), or AS between 1988 and 2006 were abstracted. Since AS patients may differ from surgically managed patients, we relied on propensity-score matched analysis to circumvent the potential biases related to population differences. Competing-risks regression analyses predicting cancer-specific mortality (CSM), after accounting for other covariates, including other-cause mortality (OCM), were fitted. A sub-analysis was conducted in patients aged >75 years. RESULTS Overall, 1007 AS patients vs. 5935 and 13721 PN and RN patients were identified, respectively. Following propensity-score matched analysis, the five-year CSM rates, after adjusting for OCM, were 4.6 vs. 4.2 vs. 22.0% for PN, RN, and AS, respectively (P<0.001). In elderly patients (>75 years), the five-year CSM rates were 7.4 vs. 6.1 vs. 29.1% for the same groups, respectively (P<0.001). In competing-risks regression analyses, PN and RN patients were both 60% less likely to die of CSM than AS patients (both P<0.001), even after accounting for OCM. In patients >75 years, PN and RN individuals were 64 and 59% less likely to die of CSM than AS patients (both P<0.003). CONCLUSIONS Surgical management remains an important consideration in localized RCC, even in elderly patients (>75 years), despite accounting for OCM. © 2012 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 187Issue 4SApril 2012Page: e524 Advertisement Copyright & Permissions© 2012 by American Urological Association Education and Research, Inc.MetricsAuthor Information Maxine Sun Montreal, Canada More articles by this author Marco Bianchi Milan, Italy More articles by this author Jens Hansen Hamburg, Germany More articles by this author Quoc-Dien Trinh Detroit, MI More articles by this author Nawar Hanna Montreal, Canada More articles by this author Markus Graefen Hamburg, Germany More articles by this author Francesco Montorsi Milan, Italy More articles by this author Paul Perrotte Montreal, Canada 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 | 0,011 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,005 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 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 ».