Eye gaze patterns differentiate novice and experts in a virtual laparoscopic surgery training environment
Bibliographic record
Abstract
Visual information is important in surgeons' manipulative performance especially in laparoscopic surgery where tactual feedback is less than in open surgery. The study of surgeons' eye movements is an innovative way of assessing skill, in that a comparison of the eye movement strategies between expert surgeons and novices may show important differences that could be used in training. We conducted a preliminary study comparing the eye movements of 5 experts and 5 novices performing a one-handed aiming task on a computer-based laparoscopic surgery simulator. The performance results showed that experts were quicker and generally committed fewer errors than novices. We investigated eye movements as a possible factor for experts performing better than novices. The results from eye gaze analysis showed that novices needed more visual feedback of the tool position to complete the task than did experts. In addition, the experts tended to maintain eye gaze on the target while manipulating the tool, whereas novices were more varied in their behaviours. For example, we found that on some trials, novices tracked the movement of the tool until it reached the target.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| 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 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".