Achieving proficiency with robot-assisted radical prostatectomy: Laparoscopic-trained versus robotics-trained surgeons
Bibliographic record
Abstract
BACKGROUND: Initiating a robotics program is complex, in regards to achieving favourable outcomes, effectively utilizing an expensive surgical tool, and granting console privileges to surgeons. We report the implementation of a community-based robotics program among minimally-invasive surgery (MIS) urologists with and without formal robotics training. METHODS: From August 2008 to December 2010 at Kaiser Permanente Southern California, 2 groups of urologists performing robot-assisted radical prostatectomy (RARP) were followed since the time of robot acquisition at a single institution. The robotics group included 4 surgeons with formal robotics training and the laparoscopic group with another 4 surgeons who were robot-naïve, but skilled in laparoscopy. The laparoscopic group underwent an initial 7-day mentorship period. Surgical proficiency was measured by various operative and pathological outcome variables. Data were evaluated using comparative statistics and multivariate analysis. RESULTS: A total of 420 and 549 RARPs were performed by the robotics and laparoscopic groups, respectively. Operative times were longer in the laparoscopic group (p = 0.002), but estimated blood loss was similar. The robotics group had a significantly better overall positive surgical margin rate of 19.9% compared to the laparoscopic group (27.8%) (p = 0.005). Both groups showed improvements in operative and pathological parameters as they accrued experience, and achieved similar results towards the end of the study. CONCLUSIONS: Robot-naïve laparoscopic surgeons may achieve similar outcomes to robotic surgeons relatively early after a graduated mentorship period. This study may apply to a community-based practice in which multiple urologists with varied training backgrounds are granted robot privileges.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".