Current evidence between hospital volume and perioperative outcome: Prospective assessment of robotic radical prostatectomy safety profile in a regional center of medium annual caseload
Bibliographic record
Abstract
INTRODUCTION: We aimed to present the safety profile of robotic radical prostatectomy (RARP) performed in a single center of medium surgical volume since its introduction and identify predictors of postoperative complications. METHODS: We prospectively collected clinical data from 317 consecutive patients undergoing RARP between August 2011 and November 2019 in a medium-volume center. Surgical procedures were performed by a single experienced surgeon. Complications were collected according to the Martin criteria for reporting and the Clavien-Dindo classification for rating. Preoperative, intraoperative, and postoperative data were analyzed and compared with available literature. RESULTS: A total of 102 complications were observed in 96 (30.3%) patients and were minor in 84.4% of cases (Clavien grade 1 and 2). Transfusion rate was 1.3%. Complications of grade 4b or 5 did not occur. The most frequent complications were urinary retention (7.3%) and anastomotic leak (5.9%). At multivariate analysis, the nerve-sparing technique was an independent predictor of complications (odds ratio [OR] 0.55, p=0.02). CONCLUSIONS: The study shows that a high safety profile may be achieved in a medium-volume hospital. The nerve-sparing technique was a predictor of complications. Further studies are needed to define the current relationship between surgical volume and perioperative outcome for RARP.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".