Oncological benefit of re‐resection for T1 bladder cancer: a comparative effectiveness study
Bibliographic record
Abstract
OBJECTIVES: To quantify the real-world survival benefit of re-resection vs no re-resection in patients diagnosed with T1 bladder cancer (BC) at the population level. PATIENTS AND METHODS: Retrospective population-wide observational cohort study based on pathology reports linked to health administrative data. We identified patients who were diagnosed with T1 BC in the province of Ontario (01/2001-12/2015) and used billing claims to ascertain whether they received re-resection within 2-10 weeks. The time-dependent effect of re-resection on survival outcomes was modelled by Cox proportional hazards regression (unadjusted and adjusted for numerous assumed patient- and surgeon-level confounding variables). Effect measures were presented as hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS: We identified 7666 patients of which 2162 (28.7%) underwent re-resection after a median (interquartile range) time of 45 (35-56) days. Patients who received re-resection were less likely to die from any causes (HR 0.68, 95% CI 0.63-0.74, P < 0.001) and from BC (HR 0.66, 95% CI 0.57-0.76, P < 0.001) during any time of follow-up. After adjusting for all assumed confounding variables, re-resection was still significantly associated with a lower overall mortality (HR 0.88, 95% CI 0.81-0.95, P < 0.001), while the association with cancer-specific survival marginally lost its statistical significance (HR 0.87, 95% CI 0.75-1.02, P = 0.08). CONCLUSIONS: A second transurethral resection within 2-6 weeks after the initial resection (i.e. re-resection) is recommended for patients diagnosed with primary T1 BC as prior studies suggest therapeutic, diagnostic, and prognostic benefits. However, results on survival endpoints are sparse, conflicting, and often affected by various biases. To the best of our knowledge, the present population-wide study represents the largest cohort of patients diagnosed with T1 BC and provides real-world evidence supporting the utilisation of re-resection in this group of patients.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".