Effect of Prostate Gland Size on the Learning Curve for Robot-Assisted Laparoscopic Radical Prostatectomy: Does Size Matter Initially?
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Widespread introduction of robot-assisted laparoscopic radical prostatectomy (RALRP) has led to multiple surgeons going through the learning curve (LC). One of the recommendations for surgeons on the LC for RALRP is to choose patients with smaller glands. We evaluated our LCs to determine whether prostate size influenced intraoperative outcomes and positive surgical margin rates. PATIENTS AND METHODS: Data were obtained from a prospective database for the first 154 cases of RALRP performed by a single surgeon. Patients were divided into three groups based on prostate volume (PV): <40 cc (group 1), 40 to 60 cc (group 2), or >60 cc (group 3). PV was estimated by preoperative transrectal ultrasonography (TRUS) and correlated with pathologic weight (PW). Perioperative and immediate postoperative outcomes were evaluated. RESULTS: A statistically significant difference in total operative times between the groups (206 minutes vs 201 minutes vs 233 minutes for groups 1, 2, and 3, respectively) was noted. With regard to individual intraoperative steps, the bladder neck reconstruction and anastomosis time was longer in group 3. No other statistically significant differences were noted. The Pearson correlation coefficient between PV estimation by TRUS and PW was r = 0.785, and an additional analysis based on PW supports the results of our study. CONCLUSIONS: Prostate size influenced total operative times and the bladder neck reconstruction and anastomosis time. Our data support the use of preoperative TRUS to estimate PV and recommendations for surgeons starting on their LC to choose glands less than 60 cc.
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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 | 0.003 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".