Robotic pyeloplasty: experience with three robotic platforms.
Bibliographic record
Abstract
PURPOSE: Laparoscopic pyeloplasty is a technically challenging procedure. Currently, several robotic surgical systems exist to overcome laparoscopic technical challenges confronted during pyeloplasty. We present a clinical comparison between three robotic surgical systems (Aesop, Zeus and da Vinci) in assisting laparoscopic pyeloplasty procedures. METHODS: From January 2002 to August 2005, 32 dismembered laparoscopic pyeloplasties were performed using three robotic surgical systems. The results of the initial six, five and nine laparoscopic robotic pyeloplasty procedures performed using the Aesop, Zeus and da Vinci platforms were compared. Data relating to the subsequent 12 pyeloplasties using the da Vinci system were also analyzed. RESULTS: The da Vinci robot required significantly more time to set up initially than the Aesop platform (12.5 min versus 39 min, p < 0.05) but the time was similar to that for the Zeus robot. Despite the longer setup time, laparoscopic robotic pyeloplasties performed using the da Vinci robot required 168 min and 35 min for operating time and anastomotic time, respectively. This was significantly faster than that for Aesop (262 min and 75 min) and Zeus (225 min and 71 min) robots (p < 0.05). There were no intra-operative complications. There was only one postoperative complication in the Zeus group involving a delayed urine leak. Narcotic requirements were low and duration of hospital stay was short for all patients. CONCLUSIONS: We show that not all advanced robotic platforms are equal. In this study, procedures performed using the da Vinci robotic system resulted in decreased anastomotic and operating times. With emerging surgical technologies, the role of the robot in surgery continues to be defined.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".