Assessment of changes in gaze patterns during training in point-of-care ultrasound
Bibliographic record
Abstract
BACKGROUND: Point-of-care ultrasound (POCUS) is a core skill in emergency medicine (EM), however, there is a lack of objective competency measures. Eye-tracking technology is a potentially useful assessment tool, as gaze patterns can reliably discriminate between experts and novices across medical specialties. We aim to determine if gaze metrics change in an independent and predictable manner during ultrasound training. METHODS: A convenience sample of first-year residents from a single academic emergency department was recruited. Participants interpreted 16 ultrasound videos of the focused assessment with sonography for trauma (FAST) scan while their gaze patterns were recorded using a commercially available eye-tracking device. The intervention group then completed an introductory ultrasound course whereas the control group received no additional education. The gaze assessment was subsequently repeated. The primary outcome was total gaze duration on the area of interest (AOI). Secondary outcomes included time to fixation, mean duration of first fixation and mean number of fixations on the AOI. RESULTS: 10 EM residents in the intervention group and 10 non-EM residents in the control group completed the study. After training, there was an 8.8 s increase in the total gaze time on the AOI in the intervention group compared to a 4.0 s decrease in the control group (p = .03). EM residents were also 3.8 s quicker to fixate on the AOI whereas the control group became 2.5 s slower (p = .04). There were no significant interactions on the number of fixations (0.43 vs. 0.18, p = .65) or duration of first fixation on the AOI (0.02 s vs. 0.06 s, p = .63). CONCLUSIONS: There are significant and quantifiable changes in gaze metrics, which occur with incremental learning after an ultrasound course. Further research is needed to validate the serial use of eye-tracking technology in following a learner's progress toward competency in point-of-care ultrasound image interpretation.
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How this classification was reachedexpand
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 | 0.002 |
| 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) | 0.003 | 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 teacher head, 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".