Development and Use of an Interactive Computerized Dog Model to Evaluate Cranial Nerve Knowledge in Veterinary Students
Bibliographic record
Abstract
In veterinary medicine, the cognitive skills necessary to interpret neurological disorders from text-based case descriptions may not translate into the diagnostic capabilities required for clinical neurological patients. As live animals exhibiting certain specific neurological disorders are infrequent during a student's exposure to clinics, students may graduate without the experience necessary to make an accurate diagnosis in the field. To address this, we have developed a computerized simulated dog head that can exhibit cranial nerve dysfunctions and respond to specific testing procedures in a clinically accurate manner. To evaluate whether this type of model could add value to traditional student assessments, we created a multiple-choice quiz system with three types of questions: standard text-based cases, videos of an expert performing an examination of the simulated dog, and an interactive version requiring the student to perform an appropriate examination of the simulated dog to uncover the lesion localization. In an experiment conducted with 97 freshman veterinary students who had recently been taught cranial nerve anatomy and function, we found that examination performance decreased with the need for interactivity compared to memorization of fact, while satisfaction increased. Students were less likely to identify the correct disorder when they had to conduct the examination of the virtual dog themselves, revealing an inadequacy in traditional neuroanatomical teaching. However, students overwhelmingly supported the use of interactive question for assessment. Interestingly, performance on text-based questions did not correlate significantly with interactive or video questions. The results have implications for veterinary teaching and assessment within the classroom and in clinical environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".