Laparoscopic robotic pyeloplasty using the Zeus Telesurgical System.
Bibliographic record
Abstract
We present the initial clinical experience using a robot to perform a laparoscopic dismembered pyeloplasty at a Canadian centre. Five patients were confirmed to have ureteropelvic junction obstructions through nuclear renography, cross sectional imaging and intravenous pyelography. After performing a retrograde ureteropyelography and double J stent placement, laparoscopic dismembered pyeloplasty was performed by a single surgeon at a remote workstation using the ZeusTM Telepresence Surgery System (Intuitive Surgicala). The mean total operative time was 225+/-48 minutes, anastomotic time was 71+/-16 minutes, and the mean time required to set-up the robot was 30+/-17 minutes. The estimated blood loss was less than 100 ml in each case. A mean total of 22+/-10 mg of morphine sulfate equivalents were used for analgesia, and the patients were discharged home after a mean of 58+/-10 hrs. There were no robotic failures, and all evaluable patients are free of pain and demonstrable obstruction. One patient developed a delayed urine leak, which resolved with percutaneous drainage. The robot provides the ability to perform complicated operations with precision through elimination of tremor, scaling of motion, and through the use of 'wristed' instruments that enhance the freedom of movement normally limited by straight-shafted laparoscopic needle drivers. The development of robotic telesurgery is still in its infancy, and the significance of its role in urologic surgery continues to be evaluated.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".