Comparison of open and robotic-assisted prostatectomy: The University of British Columbia experience
Bibliographic record
Abstract
INTRODUCTION: We assessed outcomes and costs of open prostatectomy (OP) versus robotic-assisted prostatectomy (RAP) at a single tertiary care university hospital. METHODS: We retrospectively analyzed 200 consecutive OP by 1 experienced open surgeon (MG) and 200 consecutive RAP by an experienced open surgeon (SLG), after allowing for a short learning curve of 70 cases. RESULTS: The 2 groups had similar demographics, including mean age (64.7 vs. 64.2) and mean body mass index (27.2 vs. 27.2). The OP group had a higher proportion of higher risk cancers compared to the RAP group (32.5% vs. 8.5%). Mean skin-to-skin operative room time was less for the OP (114.2 vs. 234.1 minutes). Transfusion rates were similar at 1.5% with OP compared to 3.5% with RAP. The mean length of stay was 1.78 days for OP compared to 1.76 days for RAP, for the last 100 patients in each group. The OP group had more high-grade disease in the prostatectomy specimen, with Gleason ≥8 in 23.5% compared to 3.5% in the RAP group. Positive surgical margin rates were comparable at 31% for OP and 24.6% for RAP, and remained similar after stratification for pT2 and pT3 disease. The grade I and II perioperative complication rate (Clavien-Dindo classification) was lower in the OP group (8.5% vs. 20%). Postoperative stress urinary incontinence rates (4.8% for OP and 4.6% for RAP) and biochemical-free status (91.8% for OP and 96% for RAP) did not differ at 12 months post-surgery. The additional cost of RAP was calculated as $5629 per case. The main limitations of this study are its retrospective nature and lack of validated questionnaires for evaluation of postoperative functional outcomes. CONCLUSION: While hospital length of stay, transfusion rates, positive surgical margin rates and postoperative urinary incontinence were similar, OP had a shorter operative time and a lower cost compared to the very early experience of RAP. Future parallel prospective analysis will address the impact of the learning curve on these outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".