Online Case-Based Course in Veterinary Radiographic Interpretation Generates Better Short- and Long-Term Learning Outcomes than a Virtual Lecture-Based Course
Why this work is in the frame
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Bibliographic record
Abstract
Accurate interpretation of radiographs is necessary for the correct diagnosis and treatment of patients. Research has shown that active learning methods, including case-based learning, are superior to passive learning methods, such as lectures. Short-term learning outcomes were compared between two groups by enrolling 80 fourth-semester veterinary students in either an online case-based radiology course ( n = 40) or a virtual lecture-based course ( n = 40). Long-term learning outcomes were compared among three groups: one group completed case-based instruction in the fourth semester, followed by lecture-based instruction in the fourth semester ( n = 19); the second group completed only lecture-based instruction in the fourth semester ( n = 22), and the third group completed lecture-based instruction in the fourth semester, followed by case-based instruction in the fifth semester ( n = 9). Learning was assessed using a multiple-choice examination and two independently written small animal radiograph reports. In the fourth semester, students completing the case-based course had higher examination scores and radiograph report scores than students who took the lecture-based course. Students completing the lecture-based course in the fourth semester and the case-based course in the fifth semester wrote better radiograph reports than students who completed both courses in the fourth semester; both groups wrote better reports than students who did not take the case-based course. A case-based diagnostic imaging course may be better than a lecture-based course for both short- and long-term retention of knowledge; however, there is a significant loss of knowledge following an instructional gap, and spaced refreshers may boost retention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 it