Athermal Robotic Technique of prostatectomy in patients with large prostate glands (>75 g): technique and initial results
Bibliographic record
Abstract
OBJECTIVE: To report our experience with robotic radical prostatectomy (rRP) for prostate glands of >75 g, as this technique is developing rapidly. PATIENTS AND METHODS: Between January 2005 and November 2005, 30 men with prostates of >75 g had rRP. Their clinicopathological and operative data were reviewed. Technical considerations for successful rRP in patients with large glands are discussed, including the importance of surgical exposure and multiple traction sutures. RESULTS: The mean (range) specimen weight was 116.1 (75.3-346.0) g, the patient age 65.0 (56-72) years, the body mass index 28.4 (21-41) kg/m2, the preoperative International Prostate Symptom Score 10 (0-32), and the prostate-specific antigen (PSA) level 7.54 (1.9-20.1) ng/mL. The clinical stage was T1c in 26 men and T2a in four. The biopsy Gleason scores were 3 + 3 = 6 in 25 men, 3 + 4 = 7 in four and 4 + 3 = 7 in one. The mean (range) estimated blood loss was 208 (100-600) mL and the operative duration 193 (150-270) min. The cancer was organ-confined in all patients and all surgical margins were negative. The mean (range) duration of indwelling catheterization was 12.7 (11-14) days. There were no complications during or after rRP, and the PSA level was undetectable in all patients after surgery. CONCLUSIONS: RP for patients with large prostates is technically challenging. The robotic approach does not appear to compromise oncological control. We show the feasibility of rRP for men with large glands.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".