PD29-02 BENCHMARKING BLADDER CANCER CARE: A REAL-LIFE POPULATION-BASED STUDY
Notice bibliographique
Résumé
You have accessJournal of UrologyCME1 Apr 2023PD29-02 BENCHMARKING BLADDER CANCER CARE: A REAL-LIFE POPULATION-BASED STUDY Nicolas Vanin-Moreno, Marlo Whitehead, and Robert Siemens Nicolas Vanin-MorenoNicolas Vanin-Moreno More articles by this author , Marlo WhiteheadMarlo Whitehead More articles by this author , and Robert SiemensRobert Siemens More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003315.02AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Radical cystectomy (RC) is a complex oncological surgical procedure and population studies of routine surgical care have suggested suboptimal results compared to high-volume centers of excellence. A previous Canadian bladder cancer quality-of-care consensus led to adoption of multiple key quality-of-care indicators with associated benchmarks created utilizing available evidence and expert opinion to inform and measure future performance. Herein we report real-life benchmark performance for the management of muscle invasive bladder cancer (MIBC) relative to expert opinion guidance. METHODS: This is a population-based, retrospective, cohort study that used the Ontario Cancer Registry (OCR) to identify all incident patients who underwent RC from 2009 and 2013. Electronic records of treatment from 1,573 patients were linked to OCR; pathology records were obtained for all cases and reviewed by a team of trained data abstractors. The primary objective was to describe benchmarks for identified indicators first as median values obtained across hospitals or providers as well as a “pared-mean” approach to identify a benchmark population of "top performance" as defined as the best outcome accomplished for at least 10 percent of the population. RESULTS: Overall, performance in Ontario across all indicators fell short of expert-opinion determined benchmarks. Annual surgical volume by each surgeon performing a RC (benchmark>6, percent of institutions meeting benchmark =20%), percent of patients with MIBC referred pre-operatively to Medical Oncology (MO; benchmark >90%, percent of institutions meeting benchmark =2%) and Radiation Oncology (RO; benchmark >50%, percent of institutions meeting benchmark =0%), time to cystectomy within 6 weeks of TURBT in patients without neoadjuvant chemotherapy (benchmark <6 weeks, percent of institutions meeting benchmark =0%), percent of patients with adequate lymph node dissection (defined as >14 nodes, benchmark >85%, percent of institutions meeting benchmark =0%), percent of patients with positive margins post RC (benchmark <10%, percent of institutions meeting benchmark =46%), and 90 day mortality (benchmark <5%, percent of institutions meeting benchmark =37%) fell considerably short. Simply evaluating benchmarks across the province as median performance significantly under-estimated benchmarks that were possible by top-performing hospitals. CONCLUSIONS: Performance through the majority of BC quality of care indicators fall short of benchmarks proposed by expert-opinion. Different methodologies such as a pared-mean approach of top performers may provide more realistic benchmarking. Source of Funding: This study was supported by the Institute for Clinical Evaluative Sciences (ICES), which is funded by an annual grant from the Ontario Ministry of Health and Long-Term Care (MOHLTC). Parts of this material are based on data and information compiled and provided by CIHI. © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e824 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Nicolas Vanin-Moreno More articles by this author Marlo Whitehead More articles by this author Robert Siemens More articles by this author Expand All 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 | 0,004 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».