Extended pelvic lymph node dissection at the time of robot-assisted radical prostatectomy: impact of surgical volume on efficacy and complications in a single-surgeon series.
Bibliographic record
Abstract
INTRODUCTION: We assessed the impact of surgical volume on perioperative outcomes and complications of robotic extended pelvic lymph node dissection (ePLND). METHODS: From November 2008 to October 2012, a total of 233 consecutive patients with intermediate- or high-risk clinically localized prostate cancer underwent robot-assisted radical prostatectomy (RARP) and ePLND by a single, experienced open and laparoscopic surgeon. Data were prospectively collected. Complications were classified according to the Modified Clavien System. Complications potentially related to ePLND were documented. The minimum follow-up was 3 months. To evaluate the impact of surgical volume on the results, 4 patient subgroups (subgroup 1: cases 1-59; 2: 60-117; 3: 118-175; 4: 176-233) were compared using the Chi-squared and Kruskal-Wallis tests. RESULTS: The mean (range) operative time for ePLND was 79 minutes (range: 48-144), with a steady performance over time (p = 0.784). The count of resected lymph nodes plateaued after 60 procedures (mean [range]: 13 [range: 6-32], 15 [range: 7-34], 17 [range: 8-41], 16 [range: 8-42] in Groups 1 to 4, respectively, p = 0.001). Tumour lymph node involvement was 12% in Groups 1 and 2, 7% in Group 3 and 9% in Group 4 (p = 0.075). Overall, 115 complications were reported in 98/233 patients (42%), with a significant decrease after 175 cases (p = 0.028). In Group 4, 3 patients reported an ePLND-related bleeding requiring open revision. Lymphoceles were detected in 10/233 patients (4.2%) and 1 patient (1.7%) in each of the Groups 2 to 4 required a percutaneous drainage. CONCLUSIONS: A surgeon with extensive experience is expected to achieve a safe learning curve for ePLND during RARP. A learning curve of 60 cases is suggested for optimal lymph node yield.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".