Use of Robotic Anastomosis Competency Evaluation (RACE) tool for assessment of surgical competency during urethrovesical anastomosis
Bibliographic record
Abstract
INTRODUCTION: We sought to evaluate the Robotic Anastomosis Competency Evaluation (RACE), a validated tool that objectively quantifies surgical skills specifically for urethrovesical anastomosis (UVA), as a tool to track progress of trainees, and to determine the predictive value of RACE. METHODS: UVAs performed by trainees at our institution were evaluated using RACE over a period of two years. Trainees were supervised by an experienced robotic surgeon. Outcomes included trainee-related variables (RACE score, proportion of UVA performed by trainee, and suturing speed), and clinical outcomes (total UVA duration, postoperative urinary continence, and UVA-related complications). Significance was determined using linear regression analysis. RESULTS: A total of 51 UVAs performed by six trainees were evaluated. Trainee RACE scores (19.8 to 22.3; p=0.01) and trainee proportion of UVA (67% to 80%; p=0.003) improved significantly over time. Trainee suture speed was significantly associated with RACE score (mean speed range 0.54-0.74 sutures/minute; p=0.03). Neither urinary continence at six weeks nor six months was significantly associated with RACE score (p=0.17 and p=0.15, respectively), and only one UVA-related postoperative complication was reported. CONCLUSIONS: Trainee RACE scores improved and proportion of UVA performed by trainees increased over time. RACE can be used as an objective measure of surgical performance during training. Strict mentor supervision allowed safe training without compromising patient outcomes.
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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 | 0.010 |
| 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 | 0.000 | 0.000 |
| 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".