Using the Human Patient Simulator to Educate Students of Veterinary Medicine
Bibliographic record
Abstract
INTRODUCTION: The human patient simulator has proved to be an effective educational device for teaching physicians and paramedical personnel. METHODOLOGY: To determine whether veterinary medicine students would benefit from similar educational sessions, 90 students each took a turn being the patient's clinician as real-life scenarios were played out on the simulator. The students induced and maintained anesthesia on their patient and monitored vital signs. Several critical events were presented for the students to diagnose and treat as they occurred. All students submitted a written evaluation of the course upon completion. The last 40 students were randomly divided into two groups of 20 students each. The students in Group I experienced the simulator before their clerkship examination, and those in Group II took the examination before their simulator experience. RESULTS: The students rapidly gained confidence in treating their simulated patient. This carried over to the clinical setting, where they appeared to be more confident when anesthetizing live patients. The simulator experience brought together much of the previous didactic material that they had been exposed to so they could appreciate its clinical relevance. The overwhelming response to the simulator experience was positive. The students in Group I had a significantly higher score on the clerkship examination dealing with concepts reviewed by simulation than those in Group II, who engaged in self-study instead of the simulation exercise (p < 0.001). CONCLUSION: We conclude that the human patient simulator was a valuable learning tool for students of veterinary medicine. It was exciting for the students to work with, made them deal with "real-life" scenarios, permitted them to learn without subjecting live patients to complications, enabled them to retrace their steps when their therapy did not correct the simulated patient's problems, and facilitated correlation of their basic science knowledge with clinical data, thus accelerating their ability to handle complex clinical problems in healthy and diseased patients.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".