Synchronization of Pupil Dilations Correlates With Team Performance in a Simulated Laparoscopic Team Coordination Task
Bibliographic record
Abstract
BACKGROUND: Modern surgery crucially relies on teamwork between surgeons and assistants. The science of teamwork has been and is being studied extensively although the use of specific objective methodologies such as shared pupil dilations has not been studied as sufficiently as subjective methods. In this study, we investigated team members' shared pupil dilations as a surrogate for surgeon's team performance during a simulated laparoscopic procedure. METHODS: Fourteen subjects formed dyad teams to perform a simulated laparoscopic object transportation task. Both team members' pupil dilation and eye gaze were tracked simultaneously during the procedure. Video analysis was used to identify key event movement landmarks for subtask segmentation to facilitate data analysis. Three levels of each teams' performance were determined according to task completion time and accuracy (object dropping times). The determined coefficient of determination (R2) was used to calculate the similarity in pupil dilations between 2 individual members' pupil diameters in each team. A mixed-design analysis of variance was conducted to explore how team performance level and task type were correlated to joint pupil dilation. RESULTS: The results showed that pupil dilations of higher performance teams were more synchronized, with significantly higher similarities (R2) in pupil dilation patterns between team members than those of lower performance teams (0.36 ± 0.22 vs. 0.21 ± 0.14, P < 0.001). CONCLUSIONS: Levels of pupil dilation synchronization presented among teams reflect differences in performance levels while executing simulated laparoscopic tasks; this demonstrated the potential of using joint pupil dilation as an objective indicator of surgical teamwork performance.
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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 | 0.006 |
| 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 | 0.001 |
| 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".