Cognitive load predicts point-of-care ultrasound simulator performance
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: The ability to maintain good performance with low cognitive load is an important marker of expertise. Incorporating cognitive load measurements in the context of simulation training may help to inform judgements of competence. This exploratory study investigated relationships between demographic markers of expertise, cognitive load measures, and simulator performance in the context of point-of-care ultrasonography. METHODS: Twenty-nine medical trainees and clinicians at the University of Toronto with a range of clinical ultrasound experience were recruited. Participants answered a demographic questionnaire then used an ultrasound simulator to perform targeted scanning tasks based on clinical vignettes. Participants were scored on their ability to both acquire and interpret ultrasound images. Cognitive load measures included participant self-report, eye-based physiological indices, and behavioural measures. Data were analyzed using a multilevel linear modelling approach, wherein observations were clustered by participants. RESULTS: Experienced participants outperformed novice participants on ultrasound image acquisition. Ultrasound image interpretation was comparable between the two groups. Ultrasound image acquisition performance was predicted by level of training, prior ultrasound training, and cognitive load. There was significant convergence between cognitive load measurement techniques. A marginal model of ultrasound image acquisition performance including prior ultrasound training and cognitive load as fixed effects provided the best overall fit for the observed data. DISCUSSION: In this proof-of-principle study, the combination of demographic and cognitive load measures provided more sensitive metrics to predict ultrasound simulator performance. Performance assessments which include cognitive load can help differentiate between levels of expertise in simulation environments, and may serve as better predictors of skill transfer to clinical practice.
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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 | 0.000 | 0.016 |
| 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 | 0.001 |
| 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 it