Detection of Changes in Surgical Difficulty
Bibliographic record
Abstract
BACKGROUND: Assessing the workload of surgeons requires technology to continuously monitor surgeons' behaviors without interfering with their performance. We investigated the feasibility of using eye-tracking to reveal surgeons' response to increasing task difficulty. METHODS: A controlled study was conducted in a simulated operating room, where 14 subjects were required to perform a laparoscopic procedure that includes 9 subtasks. The subtasks could be divided into 3 types with different levels of task difficulty, calculated by the index of task difficulty (ID) proposed by Fitts in 1954. Pupillary responses of subjects in performing the procedure were recorded using Tobii eye-tracking equipment. Peak pupil dilation and movement time were compared between subtasks with different IDs as well as between fast moving and slow aiming phases within each subtask. RESULTS: When the task difficulty was increased, task completion time increased. Meanwhile, the subjects' peak pupil size also increased. As the entire procedure was performed continuously, we found that pupil responses were not only affected by the ID in the current subtask but also influenced by subtasks before and after. DISCUSSION: Decomposing a surgical procedure into meaningful subtasks and examining the surgeon's pupil response to each subtask enables us to identify the challenging steps within a continuous surgical procedure. Psychomotor evidence on surgeon's performance may lead to an innovation for designing a task-specific training curriculum.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".