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Practice patterns of high-risk non-muscle invasive bladder cancer (HR-NMIBC) in real-world Canadian practice.

2024· article· en· W4399394383 on OpenAlexaffabout
Geoffrey Gotto, Nimira Alimohamed, Girish S. Kulkarni, Peter C. Black, Wassim Kassouf, Srikala S. Sridhar, Andrea Kokorovic, Bernhard J. Eigl, Normand Blais, Aly‐Khan A. Lalani, B. Osborne, Christopher J.D. Wallis

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsMcMaster UniversityBC Cancer AgencyPrincess Margaret Cancer CentreUniversity of British ColumbiaUniversity of TorontoDalhousie UniversityUniversity Health NetworkJuravinski Cancer CentreMcGill University Health CentreCentre Hospitalier de l’Université de MontréalUniversity of Calgary
FundersJanssen Biotech
KeywordsMedicineBladder cancerClinical PracticeUrothelial cancerOncologyCancerInternal medicineUrologyFamily medicine

Abstract

fetched live from OpenAlex

e16594 Background: There is a paucity of evidence pertaining to real-world treatment of localized bladder cancer, particularly in Canada. We sought to investigate real-world treatment patterns in HR-NMIBC patients, treated in Alberta to understand current treatment practices, predictors of BCG utilization and real-world survival outcomes. Methods: We conducted a retrospective, observational cohort study of de novo HR-NMIBC patients diagnosed between 2010-2022 using population-level administrative databases in Alberta. Data sources included Alberta Cancer Registry (ACR), Vital Statistics, Pharmaceutical Information Network (PIN), Health Practitioner Claims, Discharge Abstract Database (DAD) and National Ambulatory Care Report System (NACRS) databases. HR-NMIBC was defined as AJCC stage N0 and M0 with either Tis, T1, or high-grade Ta (HG-Ta). Patients were followed from diagnosis to last known contact with healthcare system, end of 2021, or death. Multivariable logistic regression analysis was used to identify features associated with receipt of BCG. Results: In this cohort, 3874 HR-NMIBC patients were identified: 82% were male and mean age was 71 years. Tumor stage was T1 in 50%, HG-Ta in 33% and Tis in 17%. Following TURBT, 60.8% of the cohort received no intravesical therapy while 35.6% received BCG treatment, 2.9% intravesical gemcitabine, and 0.6% mitomycin C. Patients who received BCG were predominantly male (83.9%), had T1 tumors (52%) and had a CCI of 0-1 (83.5%). Few (<10%) patients underwent cystectomy. In patients who received BCG, 28.3% completed only one dose 56.9% completed five induction doses, and 32% received “adequate” dosing (≥5 induction doses + ≥2 maintenance doses). In multivariable regression analysis, the strongest predictor of receipt of BCG was high-grade disease (OR 1.62; 95% CI: 1.25-2.10; p < 0.001). Other features associated with higher BCG utilization were younger age, fewer comorbidities, and rural residence and being diagnosed closer to 2010. Overall survival was 10.3 years (95% CI: 116-131 years). Conclusions: In this large, population-based retrospective study, we identify relatively poor utilization of BCG among patients with HR-NMIBC with 32% of patients receiving adequate BCG therapy, per FDA definitions. These data, while concordant with other jurisdictions, do not address underlying causes which may relate to BCG supply issues, patient preference, or fitness to receive therapy, or physician beliefs regarding treatment efficacy. Additional research is needed to identify strategies to improve utilization of guideline recommended therapy among HR-NMIBC patients in real-world settings.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.022
Threshold uncertainty score0.161

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.084
GPT teacher head0.477
Teacher spread0.393 · 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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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