Outcomes Among Rural and Urban Patients With High-Risk Nonmuscle-Invasive Bladder Cancer: Results From the Canadian Bladder Cancer Information System
Bibliographic record
Abstract
PURPOSE: Patients with high-risk nonmuscle-invasive bladder cancer (NMIBC) require frequent surveillance and adjuvant intravesical therapy, which may be less accessible in rural areas. Using the Statistics Canada Remoteness Index, we sought to investigate the effect of rurality/remoteness on the presentation, management, and surveillance of high-risk NMIBC and cancer-specific outcomes such as survival and rate of progression. MATERIALS AND METHODS: The Canadian Bladder Cancer Information System database was used to identify all patients diagnosed with high-risk NMIBC (defined as high-grade [HG] Ta, any T1 disease, CIS) on initial transurethral resection of bladder tumor. Using the manual classification method, rural areas were defined as a Remoteness Index ≥ 0.15. Exclusion criteria included patients with nonurothelial histology, unknown T stage, or evidence of nodal or distant metastases at the time of diagnosis. RESULTS: = .048). CONCLUSIONS: Rural patients with high-risk NMIBC were significantly less likely to meet quality indicator benchmarks for guideline-concordant surveillance and management, although overall rates are low indicating a potential area of quality improvement efforts.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".