An objective and automated method for assessing surgical skill in endoscopic sinus surgery using eye‐tracking and tool‐motion data
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Assessment of surgical skill plays a crucial role in determining competency, monitoring educational programs, and providing trainee feedback. With the changing health care environment, it will likely play an important role in credentialing and maintenance of certification. The ideal skill assessment tool should be unbiased, objective, and accurate. We hypothesize that tool-motion data-how a surgeon moves his/her instruments-and eye-gaze data-what a surgeon looks at when he/she operates-contain sufficient information to quantitatively and objectively evaluate surgical skill. We investigate this hypothesis by developing a statistical model of surgery and testing the model experimentally in the context of endoscopic sinus surgery (ESS). METHODS: A total of 378 trials were recorded from 7 expert and 13 novice surgeons while they were performing a series of 9 different ESS tasks. Data was collected using an electromagnetic tracker to record the surgeon's tool and endoscope motions. In addition, the location of surgeon's eye gaze was recorded using an infrared eye tracker camera. This data was fit to the statistical model and used to test the accuracy of skill assessment. RESULTS: The skill of expert surgeons was identified correctly for 94.6% of tasks. For surgeries performed by novice surgeons the proposed model properly recognizes the skill level with 88.6% accuracy. CONCLUSION: We present an objective and unbiased method for assessing the skill of endoscopic sinus surgeons. Experimental results show that the proposed method successfully identifies the skill levels of both expert and novice surgeons.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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