Positive surgical margin rates during the robot-assisted laparoscopic radical prostatectomy learning curve of an experienced laparoscopic surgeon
Bibliographic record
Abstract
INTRODUCTION: Since its introduction, robot-assisted laparoscopic radical prostatectomy (RARP) has gained widespread popularity, but is associated with a variable learning curve. Herein, we report the positive surgical margin (PSM) rates during the RARP learning curve of a single surgeon with significant previous laparoscopic radical prostatectomy (LRP) experience. METHODS: We performed a prospective cohort study of the first 400 men with prostate cancer treated with RARP by a single surgeon (BS) with significant LRP experience. Our primary outcome was the impact of case timing in the learning curve on margin status. Our analysis was conducted by dividing the case numbers into quartiles (Q1-Q4) and determining if a case falling into an earlier quartile had an impact on margin status relative to the most recent quartile (Q4). RESULTS: The Q1 cases had an odds ratio for margin positivity of 1.74 compared to Q4 (p=0.1). Multivariate logistic regression did not demonstrate case number to be a significant predictor of PSM. The mean Q1 operative time was 207.4 minutes, decreasing to 179.2 by Q4 (p<0.0001). The mean Q1 estimated blood loss was 255.1 ml, decreasing to 213.6 by Q4 (p=0.0064). There was no change in length of hospitalization within the study period. CONCLUSIONS: Even when controlling for copredictors, a statistically significant learning curve for PSM rate of a surgeon with significant previous LRP experience was not detected during the first 400 RARP cases. We hypothesize that previous LRP experience may reduce the RARP PSM learning curve.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".