Standardized 4-step technique of bladder neck dissection during robot-assisted radical prostatectomy
Bibliographic record
Abstract
Bladder neck (BN) dissection is considered one of the most challenging steps during robot-assisted radical prostatectomy. Better understanding of the BN anatomy, coupled with a standardized approach may facilitate dissection while minimizing complications. We describe in this article the 4 anatomic spaces during standardized BN dissection, as well other technical maneuvers of managing difficult scenarios including treatment of a large median lobe or patients with previous transurethral resection of the prostate. The first step involves the proper identification of the BN followed by slow horizontal dissection of the first layer (the dorsal venous complex and perivesicle fat). The second step proceeds with reconfirming the location of the BN followed by midline dissection of the second anatomical layer (the anterior bladder muscle and mucosa) using the tip of the monopolar scissor until the catheter is identified. The deflated catheter is then grasped by the assistant to apply upward traction on the prostate from 2 directions along with downward traction on the posterior bladder wall by the tip of the suction instrument. This triangulation allows easier, and safer visual, layer by layer, dissection of the third BN layer (the posterior bladder mucosa and muscle wall). The forth step is next performed by blunt puncture of the fourth layer (the retrotrigonal fascia) aiming to enter into the previously dissected seminal vesical space. Finally, both vas deferens and seminal vesicles are pulled through the open BN and handed to the assistant for upper traction to initiate Denovillier's dissection and prostate pedicle/neurovascular bundle control.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".