643 STAGE SPECIFIC POPULATION-BASED TRENDS OF RPLND USE
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Résumé
You have accessJournal of UrologyPenis/Testis/Urethra: Benign & Malignant Disease II1 Apr 2010643 STAGE SPECIFIC POPULATION-BASED TRENDS OF RPLND USE Daniel Liberman, Lars Badaus, Giovanni Lughezzani, Maxine Sun, Rodolphe Thuret, Wassim Kassouf, Hugues Widmer, Francesco Montorsi, Shahrokh F. Shariat, Paul Perrotte, and Pierre I. Karakiewicz Daniel LibermanDaniel Liberman More articles by this author , Lars BadausLars Badaus More articles by this author , Giovanni LughezzaniGiovanni Lughezzani More articles by this author , Maxine SunMaxine Sun More articles by this author , Rodolphe ThuretRodolphe Thuret More articles by this author , Wassim KassoufWassim Kassouf More articles by this author , Hugues WidmerHugues Widmer More articles by this author , Francesco MontorsiFrancesco Montorsi More articles by this author , Shahrokh F. ShariatShahrokh F. Shariat More articles by this author , Paul PerrottePaul Perrotte More articles by this author , and Pierre I. KarakiewiczPierre I. Karakiewicz More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2010.02.1017AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES No data are available regarding the use of retroperitoneal lymph node dissection (RPLND) across all stages of nonseminomatous germ cell testicular tumors (NSGCTT) in the community. We examined the rates of RPLND use in this patient subset in a large population-based cohort. METHODS Between 1988 and 2006, 9633 patients with NSGCTT were treated with or without an RPLND within 17 Surveillance, Epidemiology and End Results (SEER) registries. Analyses focused on annual trends (1988-1992, 1993-1997, 1998-2002, 2003-2006) and univariable and multivariable logistic regression models, stratified according to disease stage. Analyses were adjusted for patient age, race (white, black, other), socio-economic status (low vs. high) and SEER region. RESULTS Overall 2620 (27.2%) of 9633 NSGCTT patients underwent an RPLND (1988-1992: 34.7%, 1993-1997: 29.5%, 1998-2002: 27.6%, 2003-2006: 22.2%; X2 trend p<0.001). In patients with stage I NSGCTT, the overall rate of RPLND was 22.4% (1988-1992: 18.4%, 1993-1997: 23.5%, 1998-2002: 32.4%, 2003-2006: 25.7%; X2 trend p<0.001). In patients with stage II NSGCTT the rate of RPLND was 50.1% (1988-1992: 20.9%, 1993-1997: 18.5%, 1998-2002: 31.8%, 2003-2006: 28.8%; X2 trend p=0.003). Finally in patients with stage III NSGCTT the overall rate of RPLND was 13.1% (1988-1992: 15.0%, 1993-1997: 19.0%, 1998-2002: 32.3%, 2003-2006: 33.6%; X2 trend p=0.983). In multivariable logistic regression models that focused on stage I patients, year of surgery (p<0.001) and SEER registry (p<0.001) represented independent predictors of RPLND. In patients with stage II, year of surgery (p=0.01) remained an independent predictor of RPLND. CONCLUSIONS Population based trends indicate a decreasing rate of RPLND use. This decrease is most apparent in stage I patients where a 55.1% drop was recorded. It may be attributable to wider use of surveillance and/or chemotherapy. Less pronounced decrease was also recorded in stage II patients (19.1% drop) and is attributable to the wider use of chemotherapy as monotherapy. The RPLND rates did not change for stage III patients. Montreal, Canada© 2010 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 183Issue 4SApril 2010Page: e252 Advertisement Copyright & Permissions© 2010 by American Urological Association Education and Research, Inc.MetricsAuthor Information Daniel Liberman More articles by this author Lars Badaus More articles by this author Giovanni Lughezzani More articles by this author Maxine Sun More articles by this author Rodolphe Thuret More articles by this author Wassim Kassouf More articles by this author Hugues Widmer More articles by this author Francesco Montorsi More articles by this author Shahrokh F. Shariat More articles by this author Paul Perrotte More articles by this author Pierre I. Karakiewicz 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 | 0,002 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,004 |
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 ».