Evaluation of virtual patient cases for teaching diagnostic and management skills in internal medicine: a mixed methods study
Bibliographic record
Abstract
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 traineesâ 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 traineesâ 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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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 | 0.030 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".