Effectiveness of a Radiographic Anatomy Software Application for Enhancing Learning of Veterinary Radiographic Anatomy
Bibliographic record
Abstract
The goal of this study was to determine the effectiveness of an interactive radiology software application that we developed to enhance learning of normal canine radiographic anatomy. All first-year veterinary medical students were eligible to participate in this subject pre-test-post-test experimental design. When presented with the software application, all students had completed two terms of gross anatomy in which the complete anatomy of the dog had been taught using a combination of lectures and laboratory dissections, including radiographic examples. The software application was divided into four body regions: front limb, hind limb, skull/spine, and thorax/abdomen, each with a learning mode and a quiz mode. Quizzes were composed of 15 questions drawn pseudo-randomly without repeat from all structures within a region (median 206 structures). Students were initially given the software application with only the quiz mode activated. After completing four quizzes, one for each body region, students were given access to the software application with both learning mode and quiz mode activated. Students were instructed to spend 30 minutes using the learning mode to study the radiographic anatomy of each region and to retake each quiz. Quiz scores after using the learning mode were significantly higher for each body region (p<.001), with a large effect size for all four regions (Cohen's d=0.83-1.56). These results suggest that this radiographic anatomy software application is an effective tool for students to use to learn normal radiographic anatomy.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".