3D Digital and Printed Hearts from Different Canine Breeds as an Educational Tool for Radiographic Interpretation
Bibliographic record
Abstract
Three-dimensional (3D) printing is a new method of creating anatomical models, which can enhance the training of students and health professionals. The large breed-variation in dogs means that interpretation of thoracic radiographs can be challenging for the inexperienced radiologist. The aim of this study was to develop digital and printed 3D cardiac models from six canine breeds and evaluate their use as a tool for studying breed variations in radiology. The printed and digital 3D cardiac models were used by postgraduate veterinary students in diagnostic imaging along with a theoretical class on the subject and students completed a pre- and post-test, assessing cardiac size on thoracic radiographs in order to verify the usefulness of the models. The students then completed a satisfaction questionnaire using a Likert scale. There was a significant difference between the pre-test and the post-test results, with greater accuracy after using the 3D models. More errors were made in pre-test interpretation of radiographs from English Cocker Spaniel, English Bulldog, and Yorkshire Terrier and there were a significantly higher number of correct answers after using the 3D models. The vast majority of responses to all questions in the satisfaction questionnaire were positive, with partial or total agreement of the participants. This study demonstrates that digitally printed cardiac models from different breeds of dogs are effective learning tools. They helped students to better understand the relevant spatial relationship and cardiac morphology and to compare this anatomy with the radiographic image. Models are provided in 3D PDF and STL files for download.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".