Initial experience with robotic-assisted laparoscopic radical prostatectomy in the Canadian health care system
Bibliographic record
Abstract
BACKGROUND: Robotic-assisted laparoscopic radical prostatectomy (RALRP) has gained popularity in the United States due to claims of its superior 3-dimensional magnified vision and improved manual dexterity for surgeons that shorten the learning curve and facilitate the transition from standard open radical prostatectomy to laparoscopic prostatectomy as a minimally invasive procedure. The Canadian health care system, however, faces unique challenges when dealing with the introduction of new technologies. We report the initial experience with the use of the da Vinci robot for RALRP at the University of Western Ontario. METHODS: We retrospectively reviewed the records of the initial 30 cases of RALRP with a minimum of 6 months follow-up. Data included the surgical times of various operative segments from cases 1-15 and 16-30, perioperative complications, early oncology and early functional results. RESULTS: The lack of dedicated resources initially led to sporadic and infrequent cases. Nevertheless, there was improvement in surgical proficiency with significant difference in operative times between cases 1-15 and 16-30. Perioperative complications, though significant, were commensurate with reported early experiences from other centres worldwide, which reflects the learning curve with RALRP. CONCLUSION: Initiating a new surgical program that involves significant capital and maintenance costs, such as an RALRP program, within the Canadian health care system poses unique challenges for the surgical team. Nevertheless, our initial experience has encouraged us to proceed with the next phase of evaluation for the urological and oncological application of the technology.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".