The Effect of Adverse Patient Characteristics on Perioperative Outcomes in Open and Robot-Assisted Radical Prostatectomy
Bibliographic record
Abstract
Objective: To analyze the effect of adverse preoperative patient and tumor characteristics on perioperative outcomes of open (ORP) and robot-assisted radical prostatectomy (RARP). Material and Methods: We retrospectively analyzed 656 patients who underwent ORP or RARP according to intraoperative blood loss (BL), operation time (OR time), neurovascular bundle preservation (NVBP) and positive surgical margins (PSM). Univariable and multivariable logistic regression models were used to identify risk factors for impaired perioperative outcomes. Results: Of all included 619 patients, median age was 66 years. BMI (<25 vs. 25-30 vs. ≥30) had no influence on blood loss. Prostate size >40cc recorded increased BL compared to prostate size ≤40cc in patients undergoing ORP (800ml vs. 1200ml, p<0.001), but not in patients undergoing RARP (300ml vs. 300ml, p=0.2). Similarly, longer OR time was observed for ORP in prostates >40cc, but not for RARP. Overweight (BMI 25-30) and obese ORP patients (BMI ≥30) showed longer OR time compared to normal weight (BMI <25). Only obese patients, who underwent RARP showed longer OR time compared to normal weight. NVBP was less frequent in obese patients, who underwent ORP, relative to normal weight (25.8% vs. 14.0%, p<0.01). BMI did not affect NVPB at RARP. No differences in PSM were recorded according to prostate volume or BMI in ORP or RARP. In multivariable analyses, patient characteristics such as prostate volume and BMI was an independent predictor for prolonged OR time. Moreover, tumor characteristics (stage and grade) predicted worse perioperative outcome. Conclusion: Patients with larger prostates and obese patients undergoing ORP are at risk of higher BL, OR time or non-nervesparing procedure. Conversely, in patients undergoing RARP only obesity is associated with increased OR time. Patients with larger prostates or increased BMI might benefit most from RARP compared to ORP.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".