Early and Increased Training in Veterinary Radiology Increases Student Interest in the Specialty But May Provide Little Short-Term Gain in Radiology Knowledge
Bibliographic record
Abstract
There is a lack of consensus among educators regarding the ideal structure of radiology training in veterinary medicine. Research in the medical field suggests that early integration has positive short- and long-term impacts on student interest in radiology. This study evaluated the effect of a new radiology course in the first year of the veterinary curriculum. Authors hypothesized that students taught radiology in years 1 and 2 would have greater interest in and appreciation for the specialty of radiology and would perform better on tests of basic knowledge of medical imaging principles, entry-level image interpretation, and anatomy identification than students who were not taught until year 2. An online questionnaire was administered to different classes of students after completion of their radiology courses. Students with early and increased radiology training were significantly more likely to respond that radiology was more interesting than other veterinary specialties. Unexpectedly, students with early and increased training performed significantly better than students with less and later training on only one out of nine content knowledge questions, though they did perform significantly better on additional knowledge questions compared to students with only early exposure. This suggests early and increased training in radiology may increase student interest in and appreciation for the specialty, but may not lead to increased short-term knowledge retention compared to a traditional curriculum format.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".