Student Perceptions of Sectional CT/MRI Use in Teaching Veterinary Anatomy and the Correlation with Visual Spatial Ability: A Student Survey and Mental Rotations Test
Bibliographic record
Abstract
Diagnostic imaging technology is becoming more advanced and widely available to veterinary patients with the growing popularity of veterinary-specific computed tomography (CT) and magnetic resonance imaging (MRI). Veterinary students must, therefore, be familiar with these technologies and understand the importance of sound anatomic knowledge for interpretation of the resultant images. Anatomy teaching relies heavily on visual perception of structures and their function. In addition, visual spatial ability (VSA) positively correlates with anatomy test scores. We sought to assess the impact of including more diagnostic imaging, particularly CT/MRI, in the teaching of veterinary anatomy on the students' perceived level of usefulness and ease of understanding content. Finally, we investigated survey answers' relationship to the students' inherent baseline VSA, measured by a standard Mental Rotations Test. Students viewed diagnostic imaging as a useful inclusion that provided clear links to clinical relevance, thus improving the students' perceived benefits in its use. Use of CT and MRI images was not viewed as more beneficial, more relevant, or more useful than the use of radiographs. Furthermore, students felt that the usefulness of CT/MRI inclusion was mitigated by the lack of prior formal instruction on the basics of CT/MRI image generation and interpretation. To be of significantly greater use, addition of learning resources labeling relevant anatomy in tomographical images would improve utility of this novel teaching resource. The present study failed to find any correlation between student perceptions of diagnostic imaging in anatomy teaching and their VSA.
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 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".