Video replay in surgery: Can we make the “right call” in predicting outcomes?
Bibliographic record
Abstract
n their article, Goldenberg et al set out to answer an important question in surgical education: Can we assess technical performance in the operating room to predict clinical outcomes?The authors compared assessments by expert raters (robotic and open surgeons) and those from lay people from the Crowd-Sourced Assessment of Technical Skills (C-SATS) 1,2 group with regard to experienced surgeons' technical ability during a robotic-assisted radical cystectomy.The authors prepared short video segments (60 seconds) showing mobilization of the ureter, ureteral preparation for the anastomosis, and the ureteral-ileal anastomosis from nine (out of a potential 102) cases that resulted in clinically significant postoperative uretero-ileal strictures (UIS) (10 strictures in total).They compared these to video segments showing the same steps from eight control cases that did not result in UIS.Of note, the control group consisted of the same patients, but the video was of the procedure on the contralateral ureter that did not develop a stricture.Five content experts rated each step on a five-item dichotomous (yes/no) questionnaire developed for this study.The questionnaire assessed perceived risk for UIS during each step and overall.The C-SATS group (2142 lay people) used the Global Evaluative Assessment of Robotic Skills (GEARS) global rating scale to assess the videos.Both the authors and the C-SATS workers found there was no association between the experts' scores and UIS.The authors have taken on an ambitious task of trying to predict a clinical outcome based on surgical performance.Great gains have been made in technical skills assessment; however, the majority of the literature has reported on surgical performance in the ex-vivo operative environment (i.e., surgical simulation laboratories).To date, largely due to ethical issues, there has been a paucity of studies that evaluate intraoperative assessment.As academic surgeons in urology, we owe it to our trainees, and more importantly to our patients, to further improve surgical performance and ultimately clinical 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 | 0.009 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".