Evaluation of a 3D Computer Model of the Equine Paranasal Sinuses as a Tool for Veterinary Anatomy Education
Bibliographic record
Abstract
Detailed knowledge of anatomical systems is vital for clinical veterinary practice. However, students often find it difficult to transfer skills learned from textbooks to real-life practice. In this study, a three-dimensional computer model representing equine paranasal sinus anatomy (3D-ESM) was created and evaluated for its contribution to student understanding of the 3D dynamic nature of the system. Veterinary students and equine professionals at the University of Bristol were randomly allocated into experimental (3D model) and control (2D lecture) groups. A pre-/post-study design was used to evaluate the efficacy of the 3D model through a pre–/post–multiple-choice question (MCQ) anatomical knowledge exam and a pre-/post-questionnaire gathering information on participant demographics, confidence, and satisfaction. No statistically significant difference was found between 3D and 2D groups’ post-MCQ exam scores ( t39 = 1.289, p = .205). 3D group participant feedback was more positive than 2D group feedback, and 3D group satisfaction scores on Likert questions were significantly higher ( t118 = −5.196, p < .001). Additionally, confidence scores were significantly higher in the 3D group than in the 2D group immediately following the study ( p < .05). Participants’ open-text responses indicated they found the 3D model helpful in learning the complex anatomy of the equine paranasal sinuses. Findings suggest the 3D-ESM is an effective educational tool that aids in confidence, enjoyment, and knowledge acquisition. Though it was not better than traditional methods in terms of anatomy knowledge exam scores, the model is a valuable inclusion into the veterinary anatomy curriculum.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".