Evaluation of virtual patient cases for teaching diagnostic and management skills in internal medicine: a mixed methods study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: The virtual patient (VP) is a computer program that simulates real-life clinical scenarios and allows learners to make diagnostic and therapeutic decisions in a safe environment. Although many VP cases are available, few focus on junior trainees as their target audience. In addition, there is wide variability in trainees' clinical rotation experiences, based on local practice and referral patterns, duty hour restrictions, and competing educational requirements. In order to standardize clinical exposure and improve trainees' knowledge and perceived preparedness to manage core internal medicine cases, we developed a pool of VP cases to simulate common internal medicine presentations. We used quantitative and qualitative analyses to evaluate the effectiveness of one of our VP cases among medical trainees at University of Toronto. We also evaluated the role of VP cases in integrated teaching of non-medical expert competencies. RESULTS: Despite modest effects on knowledge acquisition, a majority of participants enjoyed using VP cases as a resource to help them prepare for and reinforce clinical experiences. Cognitive interactivity and repetitive practice were particularly appreciated by study participants. Trainees perceived VP cases as a useful resource as their learning can be customized to their actions within the case, resulting in unique learning trajectories.
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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 | 0.017 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 it