Training Surgeons’ Visual Scanning Pattern in Laparoscopic Surgery to Enhance Patient Safety
Bibliographic record
Abstract
OBJECTIVE: This study aimed to develop a novel approach for teaching visual scanning in surgery by integrating eye-tracking technology into a laparoscopic simulation environment. DESIGN: A prospective controlled study was conducted in a surgical simulation setting. PARTICIPANTS: Fifteen novice participants with no prior laparoscopic experience were recruited. SETTING: Participants performed simulated laparoscopic cholecystectomy across six training sessions. The control group received conventional technical training, while the experimental group also viewed eye-tracking videos from expert surgeons and received targeted instruction on improving environmental awareness. Visual scanning patterns were assessed based on the percentage of eye fixations directed toward the surgical equipment panel and patient vital signs, in addition to the primary surgical monitor. Task completion times were recorded across sessions to evaluate learning curves and compare performance between the two groups. RESULTS: Participants in the experimental group demonstrated significantly higher rates of eye scanning directed toward environmental inputs. Task completion times did not differ significantly between groups. CONCLUSION: Eye-tracking technology proved effective in training visual scanning pattern in image-guided surgery. These findings support early integration of patient safety skills alongside conventional technical training.
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.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".