Novel Hands-Free Pointer Improves Instruction Efficiency in Laparoscopic Surgery
Bibliographic record
Abstract
To improve instruction efficiency during advanced laparoscopic surgery, a hands-free, head-controlled, multimonitor pointer was developed. One instructor guided 20 trainees to locate critical points on a simulated laparoscopic cholecystectomy model. Twenty points, visible to the instructor only, were selected on a photo of a partially dissected gallbladder placed within a laparoscopic trainer box. For each trainee, the points were randomized to 2 groups of 10 points with the instructor providing verbal guidance only or guidance assisted by the head-controlled pointer that appeared on both the instructor's and trainees' monitors. The primary outcome was the time to locate 10 points. Total time was shorter with the pointer than with verbal guidance alone (65 +/- 14 vs 119 +/- 34 seconds, P < .001). The average of mean individual times to locate each point was shorter with the pointer than without (5.4 +/- 0.5 vs 11.9 +/- 2.4 seconds, P < .001). The instructor's efficiency improved over time with both verbal guidance (P = .007) and with the pointer (P = .001). The benefit of pointer instruction was greater in trainees with laparoscopic experience compared with those without experience (P = .006). Use of a hands-free pointer improved instruction efficiency in simulated laparoscopy. Experienced surgeons benefited the most.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".