Low compliance with guidelines for re-staging in high-grade T1 bladder cancer and the potential impact on patient outcomes in the province of Alberta
Bibliographic record
Abstract
INTRODUCTION: Despite high-level evidence of benefit, early repeat resection (ERR) among high-grade T1 bladder cancer (HGT1-BC) patients remains low in several non-Canadian jurisdictions and rates in Canada are largely unreported. We evaluated rates of ERR and trends over time in Alberta. We also examined factors associated with uptake of ERR. METHODS: We conducted a retrospective review of data from all patients diagnosed with HGT1-BC from 2007 through 2011. Patients were identified from the Alberta Cancer Registry. Patients with a non-urothelial carcinoma of the bladder and those with invasion into the prostate or metastatic disease were excluded. We collected demographic and clinicopathologic information from patients' electronic medical records. RESULTS: A total of 600 patients diagnosed with HGT1-BC were included. Overall, 167 patients (27.8%) received an ERR; however, the rate increased in a non-linear fashion from 27.4% in 2007 to 37.8% in 2011. Factors associated with ERR included age <80 years (p=0.021) and centre at which the initial transurethral resection of bladder tumour (TURBT) was performed (p=0.013). Median overall survival (OS) was not reached, but five-year OS was 72.7% (95% CI 68.9, 76.5) for those who received an ERR and 55.3% (95% CI 52.5, 58.1) for those who did not. CONCLUSIONS: Use of ERR in patients with HGT1-BC is improving over time. Regional variation in practice suggests the need for implementation strategies (i.e., provincial clinical care pathways) to standardize practice and set indicators for future measurement and reporting. Targeted interventions would require further investigation around the reasons for variation in practice.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".