Action-related eye measures to assess surgical expertise
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: Eye-tracking offers a new list of performance measures for surgeons. Previous studies of eye-tracking have reported that action-related fixation is a good measuring tool for elite task performers. Other measures, including early eye engagement to target and early eye disengagement from the previous subtask, were also reported to distinguish between different expertise levels. These parameters were examined during laparoscopic surgery simulations in the present study, with a goal to identify the most useful measures for distinguishing surgical expertise. METHODS: Surgical operators, including experienced surgeons (expert), residents (intermediate), and university students (novice), were required to perform a laparoscopic task involving reaching, grasping, and loading, while their eye movements and performance videos were recorded. Spatiotemporal features of eye-hand coordination and action-related fixation were calculated and compared among the groups. RESULTS: The study included five experienced surgeons, seven residents, and 14 novices. Overall, experts performed tasks faster than novices. Examining eye-hand coordination on each subtask, it was found that experts managed to disengage their eyes earlier from the previous subtask, whereas novices disengaged their eyes from previous subtask with a significant delay. Early eye engagement to the current subtask was observed for all operators. There was no difference in action-related fixation between experienced surgeons and novices. Disengage time was strongly associated with the surgical experience score of the operators, better than both early-engage time and action-related fixation. CONCLUSION: The spatiotemporal features of surgeons' eye-hand coordination can be used to assess level of surgical experience.
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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 | 0.000 | 0.000 |
| 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) | 0.005 | 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