Retzius-Sparing Robotic-Assisted Laparoscopic Radical Prostatectomy: A Safe Surgical Technique with Superior Continence Outcomes
Bibliographic record
Abstract
PURPOSE: To report early operative outcomes and assess continence in 100 consecutive patients who underwent Retzius-sparing robotic-assisted laparoscopic radical prostatectomy (RALP). MATERIALS AND METHODS: This was a prospective, single-center, consecutive case series of 100 and 100 patients undergoing a Retzius-sparing and a conventional RALP, respectively, by a single surgeon between March 2015 and April 2017. RESULTS: Baseline patient characteristics were similar between the two groups. The Retzius-sparing approach required significantly less console time (120.0 minutes vs 144.0 minutes, p < 0.001). There were no differences between intra- and post-operative complication rates, and hospital length of stay was similar in the two groups. Incidence of positive surgical margins was nonsignificantly different between the two groups, with 17% and 13% of pT2 patients and 49% and 48% of pT3 patients in the Retzius-sparing and conventional groups, respectively. Patients in the Retzius-sparing group had significantly superior rates of achieving post-operative urinary continence (log-rank test: p < 0.001), with 20% of patients continent within the first month, compared with 8% of patients in the conventional group. The mean number of pads per day needed at 3, 6, 9, and 12 months post-operatively was also significantly lower in the Retzius-sparing group. CONCLUSIONS: Retzius-sparing RALP requires shorter console time, is oncologically safe, and leads to significantly superior continence outcomes compared with conventional RALP.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".