Online Case-Based Course in Veterinary Radiographic Interpretation Generates Better Short- and Long-Term Learning Outcomes than a Virtual Lecture-Based Course
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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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".