LiveBook: Competence Assessment with Virtual-Patient Simulations
Bibliographic record
Abstract
Virtual-patient simulators play an important role in modern medical education. These simulators provide a safe environment for learning, give contextual feedback to learners, and allow the learner to move beyond the time and space constraints of traditional face-to-face medical instruction. In this paper, we present an interactive simulation system, LiveBook. This system places learners in the role of a clinician who investigates the symptoms of a patient, asking questions (and receiving answers) in natural language. Once the learner has completed a case by selecting the most plausible diagnosis, LiveBook provides detailed feedback on the student's performance. The first LiveBook cases are currently being piloted in the area of Pediatrics and the service is available at https://live-book.org. Based on our initial experience with the pilot, we believe that LiveBook can be a valuable addition to the curriculum of future diagnosticians, enabling them to apply their knowledge and develop their diagnostic problem-solving skills in the context of realistic scenarios.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".