Development and Pilot of Case Manager: A Virtual-Patient Experience for Veterinary Students
Bibliographic record
Abstract
There is an increasing demand in veterinary education to engage students, teach and reinforce clinical reasoning, and provide access anytime/anywhere to quality learning opportunities. In addition, accrediting bodies are asking for more concrete documentation of essential clinical-skills outcomes. Unfortunately, during the clinical year in a referral hospital setting, students are at the mercy of chance regarding the types of cases they will encounter and the opportunities they will have to participate. Patient- and case-simulation technology is becoming more popular as a way to achieve these objectives in human and veterinary medical education. Many of the current options available to the veterinary medical education community to develop virtual-patient cases are too time-consuming, cost prohibitive, or difficult for the instructor or learner to use. In response, we developed a learning tool, Case Manager, which is low-cost and user-friendly. Case Manager was designed to meet the demands of veterinary education by providing students with an opportunity to cultivate clinical reasoning skills and allowing for real-time student feedback. We launched a pilot test with 37 senior veterinary medical students as part of their Small Animal Internal Medicine clinical rotation. Students reported that Case Manager increased their engagement with the material, improved diagnostic and problem-solving skills, and broadened their exposure to a variety of cases. In addition, students felt that Case Manager was superior to a more traditional, less interactive case presentation format.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".