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PD29-02 BENCHMARKING BLADDER CANCER CARE: A REAL-LIFE POPULATION-BASED STUDY

2023· article· en· W4360608188 on OpenAlexaboutno aff
Nicolas Vanin-Moreno, Marlo Whitehead, Robert Siemens

Bibliographic record

VenueThe Journal of Urology · 2023
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsBenchmarkingMedicineBladder cancerPopulationCystectomyHealth careExcellenceBenchmark (surveying)CohortCancer registryCancerMedical physicsPathologyInternal medicineManagementCartography

Abstract

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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 ...

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score0.686

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.335
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes1
Has abstractyes

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