Synchronization of Pupil Dilations Correlates With Team Performance in a Simulated Laparoscopic Team Coordination Task
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: 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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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