Quantify difference between physicians and medical students in clinical reasoning: evidence from eye-tracking
Bibliographic record
Abstract
BACKGROUND: The assessment of clinical reasoning in health trainees is vital yet poses challenges. We tracked the eye movements of participants while they were reviewing a neurological case with the goal of finding behavioral evidence to improve health education. METHODS: Eleven medical students and seventeen expert physicians were required to read a neurological case within a 150-second timeframe. The case included descriptive text, a brain CT scan, and an electrocardiogram (ECG). Participants completed a multiple-choice questions (MCQs) test after reading the case. Eye movements of participants in case reading on eleven patient-related information areas (PRIAs) were compared between experts and novices, contrasted with the remaining areas. RESULTS: Experts spent significantly more time fixating on PRIAs during case reading than novices (42.1% vs. 29.2%, adjusted p = 0.010). Experts demonstrated significantly fewer gaze shifts between Text and CT images (2.0 times) and between CT and ECG images (2.4 times) compared to novices (6.2 and 5.4 times), with adjusted p-values of 0.002 and 0.019, respectively. A positive correlation was found between the fixation rate on PRIAs and MCQs outcome (r = 0.402, p = 0.034). CONCLUSION: Eye-tracking provides rich and reliable data reflecting physicians' ability to gather patient-relevant information during patient assessment.
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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 | 0.003 | 0.449 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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 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".