Comparing the Visual Perception According to the Performance Using the Eye-Tracking Technology in High-Fidelity Simulation Settings
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Bibliographic record
Abstract
Introduction: We used eye-tracking technology to explore the visual perception of clinicians during a high-fidelity simulation scenario. We hypothesized that physicians who were able to successfully manage a critical situation would have a different visual focus compared to those who failed. Methods: A convenience sample of 18 first-year emergency medicine residents were enrolled voluntarily to participate in a high-fidelity scenario involving a patient in shock with a 3rd degree atrioventricular block. Their performance was rated as pass or fail and depended on the proper use of the pacing unit. Participants were wearing pre-calibrated eye-tracking glasses throughout the 9-min scenario and infrared (IR) markers installed in the simulator were used to define various Areas of Interest (AOI). Total View Duration (TVD) and Time to First Fixation (TFF) by the participants were recorded for each AOI and the results were used to produce heat maps. Results: Twelve residents succeeded while six failed the scenario. The TVD for the AOI containing the pacing unit was significantly shorter (median [quartile]) for those who succeeded compared to the ones who failed (42 [31–52] sec vs. 70 [61–90] sec, p = 0.0097). The TFF for the AOI containing the ECG and vital signs monitor was also shorter for the participants who succeeded than for those who failed (22 [6–28] sec vs. 30 [27–77] sec, p = 0.0182). Discussion: There seemed to be a connection between the gaze pattern of residents in a high-fidelity bradycardia simulation and their performance. The participants who succeeded looked at the monitor earlier (diagnosis). They also spent less time fixating the pacing unit, using it promptly to address the bradycardia. This study suggests that eye-tracking technology could be used to explore how visual perception, a key information-gathering element, is tied to decision-making and clinical performance.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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