Evaluation of 3D Additively Manufactured Canine Brain Models for Teaching Veterinary Neuroanatomy
Bibliographic record
Abstract
Physical specimens are essential to the teaching of veterinary anatomy. While fresh and fixed cadavers have long been the medium of choice, plastinated specimens have gained widespread acceptance as adjuncts to dissection materials. Even though the plastination process increases the durability of specimens, these are still derived from animal tissues and require periodic replacement if used by students on a regular basis. This study investigated the use of three-dimensional additively manufactured (3D AM) models (colloquially referred to as 3D-printed models) of the canine brain as a replacement for plastinated or formalin-fixed brains. The models investigated were built based on a micro-MRI of a single canine brain and have numerous practical advantages, such as durability, lower cost over time, and reduction of animal use. The effectiveness of the models was assessed by comparing performance among students who were instructed using either plastinated brains or 3D AM models. This study used propensity score matching to generate similar pairs of students. Pairings were based on gender and initial anatomy performance across two consecutive classes of first-year veterinary students. Students' performance on a practical neuroanatomy exam was compared, and no significant differences were found in scores based on the type of material (3D AM models or plastinated specimens) used for instruction. Students in both groups were equally able to identify neuroanatomical structures on cadaveric material, as well as respond to questions involving application of neuroanatomy knowledge. Therefore, we postulate that 3D AM canine brain models are an acceptable alternative to plastinated specimens in teaching veterinary neuroanatomy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".