Detailed Description of the Karolinska Technique for Intracorporeal Studer Neobladder Reconstruction
Bibliographic record
Abstract
In the last two decades, surgical techniques for intracorporeal urinary diversion have been developed with the aim of reducing surgical morbidity. Although increasing constantly, the numbers of urologists offering intracorporeal neobladder reconstruction remain limited due to the complex nature of the procedure. In this article, we aim to provide a detailed description of the surgical technique we currently use at our institution. This technique was initially developed and perfected at the Karolinska Institutet in Sweden starting in 2003. It is a reproducible surgical approach with standardized and well-defined surgical steps. We give a detailed description of the surgical steps and provide tips and tricks to address specific situations and to increase efficiency. We also review the indications, the preoperative considerations, equipment necessary, postoperative considerations, and clinical outcomes for this procedure. Finally, we provide an accompanying didactic surgical video. We believe that this standardized approach can be learned and reproduced safely by motivated robotic surgeons.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.013 |
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".