Comparison of Veterinary Student Understanding of Extrahepatic Portosystemic Shunts When Given a Pre-Lecture Activity of a Text-Only Narrative versus an Interactive Electronic Book
Bibliographic record
Abstract
Complex vascular anomalies are often difficult concepts for veterinary medical students to comprehend, as knowledge of normal anatomy, visualization of the abnormal anatomy, and understanding of the physiologic implications of that abnormality are all required to appreciate the clinical impacts of the anomaly. Access to interactive 3D models of both the normal and abnormal vasculatures may improve student comprehension. In this study, third-year veterinary medical students in a core small animal digestive diseases course completed a pre-lecture assignment consisting of a text-only narrative ( n = 100) or an interactive electronic book (e-book; n = 102) focused on extrahepatic portosystemic shunts, followed by two generative learning activities in which they described portal anatomy and extrahepatic portosystemic shunts. An optional, anonymous post-lecture learning assessment was given to both groups. Although no difference in post-lecture assessment scores was identified between the groups, students using the interactive e-book spent significantly longer on the pre-lecture assignment and activities than students in the text-only narrative group. Students in the text-only narrative group were more likely to use spatial visualization strategies during the generative learning activities than students in the e-book group. There was no correlation between time spent on the pre-lecture tasks and learning assessment score. Interactive e-books and generative learning activities may be useful adjunct pre-lecture learning tools for teaching of complex vascular anomalies to veterinary medical students.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".