Application of a laser‐guided docking system in robot‐assisted urologic surgery
Bibliographic record
Abstract
BACKGROUND: This work explores the clinical significance of a laser-guided docking system for robot-assisted urologic surgery. MATERIALS AND METHODS: Between July 2013 and June 2014, 40 patients underwent robot-assisted laparoscopic prostatectomy (RALP), and 32 patients underwent robot-assisted laparoscopic partial nephrectomy (RAPN) performed by a single surgeon. In the RALP and RAPN groups, the robot was docked in the traditional way in 20 and 16 cases, respectively. A laser guiding system was used in the other cases. The docking time and the time required to adjust the angles were recorded. RESULT: The docking time was significantly shorter for the laser-guided process performed by inexperienced nurses. The time required to adjust the angles was also lower. There were no significant differences between the processes performed by experienced nurses. CONCLUSION: A laser-guided docking system may simplify and standardize the docking process and shorten the learning curve. Copyright © 2015 John Wiley & Sons, Ltd.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".