Assessing clinical reasoning skills following a virtual patient dizziness curriculum
Bibliographic record
Abstract
OBJECTIVES: Dizziness is a common medical symptom that is frequently misdiagnosed. While virtual patient (VP) education has been shown to improve diagnostic accuracy for dizziness as assessed by VPs, trainee performance has not been assessed on human subjects. The study aimed to assess whether internal medicine (IM) interns after training on a VP-based dizziness curriculum using a deliberate practice framework would demonstrate improved clinical reasoning when assessed in an objective structured clinical examination (OSCE). METHODS: All available interns volunteered and were randomized 2:1 to intervention (VP education) vs. control (standard clinical teaching) groups. This quasi-experimental study was conducted at one academic medical center from January to May 2021. Both groups completed pre-posttest VP case assessments (scored as correct diagnosis across six VP cases) and participated in an OSCE done 6 weeks later. The OSCEs were recorded and assessed using a rubric that was systematically developed and validated. RESULTS: Out of 21 available interns, 20 participated. Between intervention (n=13) and control (n=7), mean pretest VP diagnostic accuracy scores did not differ; the posttest VP scores improved for the intervention group (3.5 [SD 1.3] vs. 1.6 [SD 0.8], p=0.007). On the OSCE, the means scores were higher in the intervention (n=11) compared to control group (n=4) for physical exam (8.4 [SD 4.6] vs. 3.9 [SD 4.0], p=0.003) and total rubric score (43.4 [SD 12.2] vs. 32.6 [SD 11.3], p=0.04). CONCLUSIONS: The VP-based dizziness curriculum resulted in improved diagnostic accuracy among IM interns with enhanced physical exam skills retained at 6 weeks post-intervention.
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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 | 0.122 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".