Single-Port versus Multiple-Port Robot-Assisted Radical Prostatectomy: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Single-port robotic-assisted radical prostatectomy has been reported as a safe and feasible technique. However, recent studies comparing single-port versus multiple-port robotic radical prostatectomy have displayed conflicting results. Objectives: To investigate the benefit of single-port robotic radical prostatectomy and the impact on outcome compared to multiple-port robotic radical prostatectomy. Methods: Based on PRISMA and AMSTAR criteria, a systematic review and meta-analysis were carried out. Finally, we considered the controlled studies with two cohorts (one cohort for single-port RARP and the other cohort for multiple-port RARP). For statistical analysis, Review Manager (RevMan) software version 5.4 was used. The Newcastle-Ottawa Scale was employed to assess the risk of bias. Results: Five non-randomized controlled studies with 666 patients were included. Single-port robotic radical prostatectomy was associated with shorter hospital stays. Only 60.6% of single-port patients (109/180) required analgesia compared to 90% (224/249) of multiple-port patients (Z = 3.50; p = 0.0005; 95% CI 0.07:0.47). Opioid administration was also significantly lower in single-port patients, 26.2% (34/130) vs. 56.6% (77/136) (Z = 4.90; p < 0.00001; 95% CI 0.15:–0.44) There was no significant difference in operative time, blood loss, complication rate, positive surgical margin rate, or continence at day 90. Conclusion: The available data on single-port robotic radical prostatectomy is very limited. However, it seems comparable to the multiple-port platform in terms of short-term outcomes when performed with expert surgeons. Single-port prostatectomies might provide a shorter hospital stay and a lower requirement for opioids; however, randomized trials with long-term follow-up are mandatory for valid comparisons.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".