What Do Surgeons See
Bibliographic record
Abstract
Recording eye motions in surgical environments is challenging. This study describes the authors' experiences with performing eye-tracking for improving surgery training, both in the laboratory and in the operating room (OR). Three different eye-trackers were used, each with different capabilities and requirements. For monitoring eye gaze shifts over the room scene in a simulated OR, a head-mounted system was used. The number of surgeons' eye glances on the monitor displaying patient vital signs was successfully captured by this system. The resolution of the head-mounted eye-tracker was not sufficient to obtain the gaze coordinates in detail on the surgical display monitor. The authors then selected a high-resolution eye-tracker built in to a 17-inch computer monitor that is capable of recording gaze differences with resolution of 1° of visual angle. This system enables one to investigate surgeons' eye-hand coordination on the surgical monitor in the laboratory environment. However, the limited effective tracking distance restricts the use of this system in the dynamic environment in the real OR. Another eye-tracker system was found with equally high level of resolution but with more flexibility on the tracking distance, as the eye-tracker camera was detached from the monitor. With this system, the surgeon's gaze during 11 laparoscopic procedures in the OR was recorded successfully. There were many logistical challenges with unobtrusively integrating the eye-tracking equipment into the regular OR workflow and data processing issues in the form of image compatibility and data validation. The experiences and solutions to these challenges are discussed.
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.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.019 |
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".