Objective Assessment of Laparoscopic Skills
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it