Utilization of Re-resection in T1 Bladder Cancer: A Population-based Study
Bibliographic record
Abstract
Introduction: T1 bladder cancer represents the aggressive end of the spectrum of non-muscle invasive bladder cancer. Based on proposed therapeutic, diagnostic, and prognostic benefits, guidelines recommend a second resection, a so-called re-resection, 2 to 6 weeks after initial transurethral resection of the bladder tumor. This thesis aims to investigate the uptake of re-resection (Objective 1), factors associated with re-resection (Objective 2), and the oncological benefit of re-resection (Objective 3) in T1 bladder cancer at the population level within the province of Ontario (Canada). Methods: Manually abstracted bladder cancer pathology reports were linked to health administrative databases to (1) identify patients diagnosed with T1 bladder cancer between January 1, 2001 and December 31, 2015, (2) to ascertain if they underwent re-resection, (3) to verify their survival, and (4) to measure covariates at diagnosis. Interrupted time series analysis was used to explore Objective 1 while multivariable logistic regression and time-dependent Cox proportional hazards regression were used to analyze Objectives 2 and 3, respectively. Results: A cohort of 7,666 patients diagnosed with T1 bladder cancer could be identified. Re-resection rates increased from 8.4% in 2001 to 28.3% in 2015 regardless of an influential guideline revision released in April 2008. Patients with a more aggressive tumor profile, individuals without sufficiently sampled muscularis propria as well as younger, healthier, and socio-economically advantaged patients were more likely to receive re-resection. Considering surgeon-specific factors, more senior, lower-volume, and male surgeons were less likely to offer re-resection to their patients. During any time of follow-up, the receipt of re-resection was associated with a lower rate of death. Conclusions: This thesis currently represents the largest cohort of patients diagnosed with T1 bladder cancer. The rather low uptake of re-resection regardless of an influential guideline revision in combination with heterogeneous uptake behavior among different groups of surgeons implies a gap in knowledge translation/exchange that needs to be addressed by tailored initiatives after identification of specific barriers to change. When it comes to the oncological benefit of re-resection, the findings of this work represent an essential piece of evidence supporting the use of re-resection in T1 bladder cancer.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".