Feasibility of expert and crowd-sourced review of intraoperative video for quality improvement of intracorporeal urinary diversion during robotic radical cystectomy
Bibliographic record
Abstract
INTRODUCTION: Development of uretero-ileal stricture (UIS) after robotic-assisted radical cystectomy (RARC) may be dependent on surgical technique. Video review of intraoperative technique is an emerging paradigm for surgical quality improvement. We examined whether surgeon-perceived risk of UIS or crowd-sourced assessment of robotic skill are associated with the development of UIS. METHODS: We conducted a case-control study comparing the operative technique of uretero-ileal anastomoses resulting in clinically significant UIS with the contralateral anastomosis for the same patient. De-identified videos were analyzed by 1) five high-volume surgeons; and 2) crowd workers (Crowd-Sourced Assessment of Technical Skill, C-SATS) to determine Global Evaluative Assessment of Robotic Skill (GEARS) score. Mantel-Haenszel common odds ratio (OR) estimates were calculated to assess the association between surgeon performance and the development of UIS. Logistic regression models were used to examine the association between GEARS scores and the development of UIS. RESULTS: A total of 10 UIS videos were compared to eight control videos by five surgeons and 2142 crowd workers. Expert surgeons systematically evaluated intraoperative footage, however, no association between the expert mode response and UIS (OR 0.42; 95% confidence interval [CI] 0.05-3.45; p=0.91) was identified. Crowd-sourced assessment was not predictive of UIS (p=0.62). CONCLUSIONS: We used video review to systematically analyze procedure-specific content and technique. The inability of surgeons to predict UIS may reflect the questionnaire, uncontrolled patient factors, or a lack of power. Crowd-sourced GEARS score was unsuccessful in predicting UIS after RARC.
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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 | 0.006 | 0.035 |
| 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 | 0.001 |
| 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".