A population-based analysis of patterns of care in patients with de novo muscle-invasive bladder cancer from Alberta, Canada
Bibliographic record
Abstract
INTRODUCTION: Approximately 25% of patients diagnosed with bladder cancer have muscle-invasive disease (MIBC). While real-world data have highlighted opportunities to improve curative-intent treatment rates, comprehensive population-level data in Canada are limited. This study aimed to assess patterns of care and outcomes in a real-world cohort of MIBC in Canada. METHODS: This retrospective, observational study describes baseline characteristics, treatment patterns, and overall survival (OS) of individuals with de novo MIBC diagnosed between 2010 and 2020 in Alberta, Canada. Data from adult patients with MIBC (T2-T4, N0/1, M0) were obtained from administrative databases and analyzed using basic statistics, multivariate regression analyses, and the Kaplan-Meier method. RESULTS: We identified 1292 patients with de novo MIBC. Of these, 76% were male with a median age of 73 years, 68% had cT2, and 76% had cN0 disease; approximately half had a Charlson comorbidity index (CCI) ≥1. Overall, 25% did not receive active treatment, while 58% received curative-intent treatment (49% underwent radical cystectomy [RC] and 9% received chemoradiotherapy), and 17% received some form of non-curative-intent treatment. Of those who underwent RC, 45% received neoadjuvant chemotherapy (NAC). Median overall survival (mOS) in the entire cohort was 2.1 years (95% confidence interval 1.9-2.4). Key predictors of inferior survival were age ≥76 years, CCI score of ≥1, T4 tumor stage, or not receiving NAC. CONCLUSIONS: This real-world analysis highlights opportunities to improve outcomes for patients with MIBC. Increasing access to curativeintent treatments, particularly in the elderly and those with comorbidities, is likely to enhance patient care and outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".