En bloc transurethral resection of bladder tumors: A review of current techniques
Bibliographic record
Abstract
INTRODUCTION: Growing interest surrounds the concept of en bloc transurethral resection of bladder tumors (ERBT). Theoretical advantages include improved adherence to oncological principles and potential yield of superior pathological specimens. Multiple ERBT methods exist. This review summarizes the current evidence regarding application of differing techniques and technologies to ERBT. METHODS: A systematic review of MEDLINE/EMBASE/Scopus databases was performed, using terms "en bloc," "ERBT," "bladder," and "urinary bladder neoplasm." Template-based data extraction included technique of ERBT, feasibility, tumor size, activation of obturator nerve reflex, operative complications, detrusor muscle sampling rate, and recurrence data. RESULTS: Multiple approaches to ERBT have evolved, using a variety of energy sources. The feasibility of electrocautery, laser, combined waterjet/electrocautery, and polypectomy snare techniques have been confirmed in achieving ERBT. ERBT appears safe, with a low complication rate. The use of laser energy sources reduces the risk of activating the obturator nerve reflex during lateral wall resections. Otherwise, no energy source is unequivocally superior in achieving ERBT. The rate of detrusor muscle sampling is high with use of ERBT and appears superior to that achieved with conventional TURBT (cTURBT) in multiple comparative studies. A limited number of largely non-randomized trials assess bladder tumor recurrence; current evidence suggests this is similar between ERBT and cTURBT groups. CONCLUSIONS: ERBT using a variety of technologies is feasible and safe, with a high detrusor muscle sampling rate. Further research is required to determine whether rates of residual disease or recurrence can be reduced with ERBT vs. cTURBT.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".