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 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 | 0.010 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".