Comparison of Clinicopathologic and Oncological Outcomes Between Transurethral En Bloc Resection and Conventional Transurethral Resection of Bladder Tumor: A Systematic Review, Meta-Analysis, and Network Meta-Analysis with Focus on Different Energy Sources
Bibliographic record
Abstract
Introduction: It has been hypothesized that transurethral en bloc (TUEB) of bladder tumor offers benefits over conventional transurethral resection of bladder tumor (cTURBT). This study aimed to compare disease outcomes of TUEB and cTURBT with focus on the different energy sources. Methods: A systematic search was performed using PubMed and Web of Science databases in June 2021. Studies that compared the pathological (detrusor muscle presence), oncological (recurrence rates) efficacy, and safety (serious adverse events [SAEs]) of TUEB and cTURBT were included. Random- and fixed-effects meta-analytic models and Bayesian approach in the network meta-analysis was used. Results: Seven randomized clinical trials (RCTs) and seven non-RCTs (NRCT), with a total of 2092 patients. The pooled 3- and 12-month recurrence risk ratios (RR) of five and four NRCTs were 0.46 (95% CI 0.29–0.73) and 0.56 (95% CI 0.33–0.96), respectively. The pooled 3- and 12-month recurrence RRs of four and seven RCTs were 0.57 (95% CI 0.25–1.27) and 0.89 (95% CI 0.69–1.15), respectively. The pooled RR for SAEs such as prolonged hematuria and bladder perforation of seven RCTs was 0.16 (95% CI 0.06–0.41) in benefit of TUEB. Seven RCTs ( n = 1077) met our eligibility criteria for network meta-analysis. There was no difference in 12-month recurrence rates between hybridknife, laser, and bipolar TUEB compared with cTURBT. Contrary, laser TUEB was significantly associated with lower SAEs compared with cTURBT. Surface under the cumulative ranking curve ranking analyses showed with high certainty that laser TUEB was the best treatment option to access all endpoints. Conclusion: While NRCTs suggested a recurrence-free benefit to TUEB compared with cTURBT, RCTs failed to confirm this. Conversely, SAEs were consistently and clinically significantly better for TUEB. Network meta-analyses suggested laser TUEB has the best performance compared with other energy sources. These early findings need to be confirmed and expanded upon.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.025 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".