Comparative effectiveness of en-bloc resection techniques vs. conventional transurethral resection for non-muscle-invasive bladder cancer
Bibliographic record
Abstract
Introduction: Transurethral en-bloc resection of bladder tumor (ERBT) has emerged as an alternate technique to conventional transurethral resection of bladder tumor (cTURBT). While theoretically advantageous, the comparative effectiveness of ERBT across various technical approaches remains unclear. We performed an updated systematic review and meta-analysis to evaluate perioperative, pathologic, and oncologic outcomes of ERBT vs. cTURBT. Methods: We systematically searched PubMed, EMBASE, Scopus, and Google Scholar for randomized controlled trials (RCTs) comparing ERBT and cTURBT. The primary outcome was recurrence-free survival (RFS). Secondary outcomes were operative time, complication rates, detrusor muscle presence, and need for repeated resection. Meta-analyses were performed, with subgroup analyses stratified by ERBT technique. Results: A total of 10 RCTs with 1973 patients (1012 ERBT, 961 cTURBT) were included. Overall data favored ERBT in RFS (hazard ratio [HR] 0.85, 95% confidence interval [CI] 0.71-1.01, p=0.07, I2=48%), with bipolar ERBT demonstrating significantly improved RFS (HR 0.51, 95% CI 0.32-0.81, p=0.004). ERBT had longer operative times compared to cTURBT (MD 3.52 minutes, 95% CI 1.25-5.80, p=0.001, I2=71%). There were no significant differences in catheter time or hospital stay between groups. ERBT had a non-significant lower incidence of bladder perforation (odds ratio [OR] 0.41, 95% CI 0.16-1.04, p=0.06, I2=52%) and obturator nerve reflex (OR 0.27, 95% CI 0.10-0.74, p=0.01, I2=79%) compared to cTURBT. ERBT was not significantly associated with higher detrusor muscle presence (OR 2.08, 95% CI 0.94-4.58, p=0.07, I2=78%). Conclusions: ERBT might have oncologic and perioperative benefits, in addition to technical advantages, relative to cTURBT. Variations in resection instruments used impact the consistency of results.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.022 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".