Objective Assessment of Laparoscopic Skills
Bibliographic record
Abstract
BACKGROUND: Assessment of surgical performance is often accomplished with traditional methods that often provide only subjective data. Trainees who perform well on a simulator in a controlled environment may not perform well in a real operating room environment with distractions. This project uses the ideas of dual-task methodology and applies them to the assessment of performance of laparoscopic surgical skills. The level of performance on distracting secondary tasks while trying to perform a primary task becomes an indirect but objective measure of the surgical skill of the trainee. METHODS: Nine surgery residents and 6 experienced laparoscopic surgeons performed 3 primary tasks on a laparoscopic virtual reality simulator (camera position, grasping, and cholecystectomy) while being distracted by 3 secondary tasks (counting beeps, selective responses, and mental arithmetic). Completion time and error rates were recorded for each combination of tasks. RESULTS: When performed separately, time to completion and error rates for primary and secondary tasks were similar for learners and experts. When performing the tasks simultaneously, learners had more errors than experts. Error rates increased for learners when distracting tasks became more difficult or required more attention. Expert surgeons maintained consistent error rates despite the increasing difficulty of task combinations. CONCLUSIONS: The use of dual-task methodology may help trainers to identify which surgical trainees require more preparation before entering the real operating room environment. Expert surgeons are capable of maintaining performance levels on a primary task in the face of distractions that may occur in the operating room.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".