Three-Dimensional Printed Models of the Heart Represent an Opportunity for Inclusive Learning
Bibliographic record
Abstract
Three-dimensional (3D) printed models of anatomic structures offer an alternative to studying manufactured, “idealized” models or cadaveric specimens. The utility of 3D printed models of the heart for clinical veterinary students learning echocardiographic anatomy is unreported. This study aimed to assess the feasibility and utility of 3D printed models of the canine heart as a supplementary teaching aid in final-year vet students. We hypothesized that using 3D printed cardiac models would improve test scores and feedback when compared with a control group. Students ( n = 31) were randomized to use either a video guide to echocardiographic anatomy alongside 3D printed models (3DMs) or video only (VO). Prior to a self-directed learning session, students answered eight extended matching questions as a baseline knowledge assessment. They then undertook the learning session and provided feedback (Likert scores and free text). Students repeated the test within 1 to 3 days. Changes in test scores and feedback were compared between 3DM and VO groups, and between track and non-track rotation students. The 3DM group had increased test scores in the non-track subgroup. Track students’ test scores in the VO group increased, but not in the 3DM group. Students in the 3DM group had a higher completion rate, and more left free-text feedback. Feedback from 3DM was almost universally positive, and students believed more strongly that these should be used for future veterinary anatomy teaching. In conclusion, these pilot data suggest that 3D printed canine cardiac models are feasible to produce and represent an inclusive learning opportunity, promoting student engagement.
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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 | 0.004 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".