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Real-world treatment practices in patients with muscle invasive bladder cancer (MIBC) treated in Alberta, Canada.

2024· article· en· W4399394385 on OpenAlexaffabout
Nimira Alimohamed, Geoffrey Gotto, 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 cancerOncologyCancerInternal medicine

Abstract

fetched live from OpenAlex

e16596 Background: Contemporary real-world data on treatment practices for patients with localized bladder cancer is limited. In this study we examined treatment patterns for patients diagnosed with de novo MIBC in Alberta. In addition, we evaluated the impact of patient demographics on treatment selection. Methods: We conducted a retrospective, observational cohort study of patients diagnosed with de novo MIBC between 2010-2022 using population-level administrative databases. Data sources included Alberta Cancer Registry, Vital Statistics, Pharmaceutical Information Network, Health Practitioner Claims, Discharge Abstract Database and National Ambulatory Care Report System databases. De novo MIBC was defined as initial presentation of UC with stage T2-T4, N0-N3, and M0 urothelial bladder cancer. Patients were followed from diagnosis to last known contact with the healthcare system, end of 2021, or death. Treatment patterns were descriptively summarized with stratified analyses performed according to treatment. Kaplan-Meier technique was used to estimate median overall survival (OS). Results: Overall, 1,292 de novo MIBC patients were identified: 76% male, mean age of 73 years. Overall, 50.3% of underwent a radical cystectomy (RC), 9% received chemoradiotherapy, 3.6% chemotherapy only, 12% radiation only, and 25% did not receive active treatment. Of those receiving RC (n=651), 51% received perioperative chemotherapy. Overall, 43.5% of the cohort received neoadjuvant systemic therapy with 12.6% receiving adjuvant systemic therapy. Stratification by treatment revealed most patients receiving radiation or no treatment were >75 years (86% and 73%, respectively) with a Charlson comorbidity index (CCI) ≥1 (64% and 67% respectively). The majority of patients receiving RC or chemoradiotherapy were ≤75 year (77% and 63%) with a CCI=0 (61% and 53%).Median OS was 26 months from MIBC diagnosis and differed by treatment group: 83.3 months for RC with perioperative chemo, 42.4 months for RC w/o chemotherapy, 24.8 months for chemotherapy + radiation, 16.2 months for chemotherapy alone, 8.6 months for no therapy and 7 months for radiation alone. Conclusions: This real-world cohort of de novo MIBC demonstrates a significant use of definitive surgical care (50%) compared to chemoradiotherapy (9%) with over one third of patients not receiving curative intent treatment. Notably, 40% of patients received neoadjuvant systemic therapy, aligning to current guideline recommended treatment strategies. Variations in care were found as treatment modalities differed considerably based on patient characteristics. While this likely reflects appropriate clinical care, selection biases limit accurate conclusions regarding efficacy. These data highlight the need to better understand drivers for treatment selection in MIBC given potential influence on outcomes.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.023
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
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 head0.085
GPT teacher head0.440
Teacher spread0.356 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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