Robotic-assisted Laparoscopic Renal and Adrenal Surgery
Bibliographic record
Abstract
Telesurgical System by ourselves 6 and others; 7,8 however, the vast majority of published data in recent years has focused almost exclusively on the da Vinci ® system.And since the da Vinci ® robot is the only master-slave robotic platform currently in production and available commercially, the focus of this chapter will center on this particular system as it applies to renal and adrenal surgical applications.While the field of urology was not the first medical discipline to embrace robotic technology, it has adopted the technology with open arms.Through innovation and research, roboticassisted surgery is quickly becoming a routine tool in the urologist's armamentarium.Currently, the majority of clinical indications for the da Vinci ® system are for urological use.The majority of published research and clinical experience in the past has focused on robotassisted radical prostatectomy.3,9,10 However, the role of robotics in renal surgery continues to be defined.With the exception of robot-assisted laparoscopic pyeloplasty (RALP), the majority of literary publications consist of case series and reports.As such, the emphasis of this chapter will be on RALP.For most other applications, the true role of robot-assisted renal surgery is yet to be defined.Herein we focus on the indications, techniques, and surgical experiences described in the literature to date as it applies specifically to roboticassisted laparoscopic renal surgery.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".