Is Robot-Assisted Radical Prostatectomy Safe in Men with High-Risk Prostate Cancer? Assessment of Perioperative Outcomes, Positive Surgical Margins, and Use of Additional Cancer Treatments
Bibliographic record
Abstract
INTRODUCTION: Despite a rapid dissemination of robot-assisted radical prostatectomy (RARP) over open radical prostatectomy (ORP), to date no study has compared perioperative outcomes between the two approaches in patients with high-risk prostate cancer (PCa). The aim of our study was to evaluate the safety and feasibility of RARP in this setting. PATIENTS AND METHODS: Overall, 1,512 patients with high-risk PCa within the Surveillance, Epidemiology, and End RESULTS (SEER) Medicare-linked database diagnosed between 2008 and 2009 were abstracted. Patients were treated with RARP or ORP. Postoperative complications, blood transfusions, prolonged length of stay (pLOS), positive surgical margins, and additional cancer therapy rates were compared. Propensity-score matched analyses and logistic regression models fitted with generalized estimating equations for clustering among hospitals were performed. RESULTS: Overall, 706 (46.7%) and 806 (53.3%) patients underwent ORP and RARP, respectively. Following propensity-matched analyses, 706 patients remained. No differences were observed in complications (P=0.6), positive surgical margins (P=0.4), or additional therapy after surgery (P=0.2) between patients treated with RARP and ORP; however, RARP was associated with lower rates of transfusions and shorter hospitalization (all P<0.001). In multivariable analyses, patients undergoing RARP were less likely to receive a blood transfusion (P=0.002) or to experience pLOS (P<0.001) compared with men treated with ORP. CONCLUSIONS: RARP and ORP have comparable complications, positive surgical margins, and additional cancer therapy rates in high-risk PCa. RARP is associated with lower rates of blood transfusions and shorter hospital stays. These findings suggest that RARP is safe and feasible even in this clinical scenario.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".