A population-based analysis of patterns of care in patients with high-risk non-muscle-invasive bladder cancer from Alberta, Canada
Bibliographic record
Abstract
INTRODUCTION: Approximately three-quarters of patients newly diagnosed with bladder cancer have non-muscle-invasive disease (NMIBC). Among these patients, those with high-risk (HR) features should be managed more aggressively in an attempt to circumvent the elevated risk of recurrence/progression. Population-based data on the incidence of HR-NMIBC and receipt of guideline-recommended care are limited. METHODS: This retrospective, observational study gathered data from multiple linked provincial (Alberta) healthcare databases to describe baseline characteristics, treatment patterns, and survival outcomes in a population of individuals diagnosed with HR-NMIBC from 2010-2020. Data for all patients aged >18 years with T1, Tis, or high-grade Ta NMIBC ("high-risk") were analyzed using basic statistics, multivariate regression analyses, and the Kaplan-Meier method. RESULTS: Of 6837 de novo NMIBC patients identified, 3874 (57%) were categorized as HR-NMIBC. The majority (82%) were male with a median age of 72 years, and approximately half had a Charlson comorbidity index score ≥1. Following initial transurethral resection of bladder tumor (TURBT), 61% of the cohort received no adjuvant bacillus Calmette-Guérin (BCG) or chemotherapy, while 36% received BCG, 3% gemcitabine, and 1% mitomycin C. Patients underwent a median of four TURBT procedures. 'Adequate BCG' (≥5 induction doses + ≥2 maintenance doses) was received by 32% of BCG-treated and 12% of all HR-NMIBC patients. Survival was improved in patients receiving adequate BCG. CONCLUSIONS: Data from this large, real-world population highlights poor use of induction/maintenance BCG therapy following TURBT among patients with HR-NMIBC.
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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 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".