Medium- to Long-Term Outcomes Following Robotic-Assisted Simple Prostatectomy
Bibliographic record
Abstract
Background/Objectives: Robotic-assisted simple prostatectomy (RASP) is an increasingly popular surgical approach for prostate enucleation. The aim of this study is to evaluate the incidence of perioperative and delayed complications following RASP and the medium- to long-term urinary function outcomes. Methods: This is a multi-centre retrospective chart analysis of patients who underwent RASP between October 2016 and October 2022. Surgery was performed using a transvesical approach with a DaVinci Xi system. Patients were reviewed pre- and postoperatively at six weeks and annually thereafter. Patient characteristics, perioperative outcomes, pre- and postoperative uroflowmetry and post-void residual (PVR) measurement were assessed. Results: A total of 50 patients with mean preoperative prostate volume of 180.3 ± 48.1 underwent RASP. The mean operative time was 140.7 ± 28.7 min and hospital length of stay was 5.2 ± 2.9 days. The mean intraoperative blood loss was 247.4 ± 153.7 mL and no patients required transfusion. The mean follow-up period was 37.2 ± 18.3 months. No patients developed stress urinary incontinence. Two patients developed delayed bladder neck contracture at 44 and 63 months. There was a significant improvement in peak urinary flow rate (Qmax) (preop Qmax 10.7 mL/s vs. postop Qmax 24.2 mL/s, p < 0.05) and PVR (preop PVR 366.5 mL vs. postop PVR 42.2 mL, p < 0.05). All patients were weaned off medical therapy for benign prostatic enlargement (BPE) and no patients had recurrent lower urinary tract symptoms requiring re-operation. Conclusions: RASP is a safe and effective enucleation technique for large prostates >100 mL with excellent long-term durability of urinary function outcomes beyond 36 months.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".