Use of Three-Dimensional Printing Models for Veterinary Medical Education: Impact on Learning How to Identify Canine Vertebral Fractures
Bibliographic record
Abstract
Vertebral fractures and luxations are common causes of neurological emergencies in small-animal patients. The objective of this study was to evaluate the impact of three-dimensional printing (3Dp) models on how veterinary students understand and learn to identify canine spinal fractures and to compare 3Dp models to computed tomography (CT) images and three-dimensional CT (3D-CT) reconstructions. Three spinal fracture models were generated by 3Dp. Sixty first-year veterinary students were randomized into three teaching module groups (CT, 3D-CT, or 3Dp) and asked to answer a multiple-choice questionnaire with 12 questions that covered normal spinal anatomy and the identification of vertebral fractures. We used four additional questions to evaluate the overall learning experience and knowledge acquisition. Results showed that students in the 3Dp group performed significantly better than those in the CT ( p < .001) and the 3D-CT ( p < .001) groups. Students in the 3Dp and 3D-CT groups answered all questions more quickly than the CT group (3Dp versus CT, p < .001; 3D-CTversus CT, p < .001), with no significant differences between the 3Dp and 3D-CT groups ( p = .051). Only the degree of knowledge acquisition that the students considered they had acquired during the session showed significant differences between groups ( p = .01). In conclusion, across first-year veterinary students, 3Dp models facilitated learning about normal canine vertebral anatomy and markedly improved the identification of canine spinal fractures. Three-dimensional printing models are an easy and inexpensive teaching method that could be incorporated into veterinary neuroanatomy classes to improve learning in undergraduate students.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".