Differences in Patient Characteristics Among Men Choosing Open or Robot-Assisted Radical Prostatectomy in Contemporary Practice at a European High-Volume Center
Bibliographic record
Abstract
OBJECTIVE: To examine the characteristics of robot-assisted radical prostatectomy (RARP) and open radical prostatectomy (ORP) patients at a high-volume center. PATIENTS AND METHODS: We relied on the Martini-Clinic database and focused on prostate cancer patients treated in 2013. Characteristics in ORP and RARP patients were assessed. In multivariable logistic regression analyses (MVA), we predicted RARP treatment. RESULTS: Of 1,920 patients, 575 (29.9%) underwent RARP and 1,345 (70.1%) ORP. RARP patients had a lower prostate-specific antigen (PSA), and were less likely to harbor pT3b, pathological Gleason ≥4 + 4 or lymph node metastases (all p < 0.05). Pelvic lymph node dissection (PLND) (84.3 vs. 87.0%, p = 0.1), as well as positive surgical margins (15.5 vs. 15.7%, p = 0.7) and the nerve-sparing status (p = 0.5) were comparable between RARP and ORP. Lymph node yield (median 11 vs. 16), and median blood loss (250 vs. 700 ml) were lower at RARP (all p < 0.001). Additionally, the median operating room time was higher at RARP (215 vs. 185 min, p < 0.001). In MVA, patients with body mass index (BMI) ≥30 were more likely to undergo RARP (OR 1.8, 95% CI 1.3-2.4, p < 0.001). Conversely, patients with PSA >20 ng/ml were less likely to undergo RARP (OR 0.6, 95% CI 0.4-1.0, p = 0.03). CONCLUSIONS: More favorable pathological characteristics were recorded at RARP. High BMI and low PSA were independent predictors for RARP. Treatment characteristics such as PLND rates, margin status and nerve sparing were comparable between RARP and ORP. Despite lower blood loss at RARP, a longer operating room time and lower yield of lymph nodes were recorded.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".