The Effectiveness of 3D Animations to Enhance Understanding of Cranial Cruciate Ligament Rupture
Bibliographic record
Abstract
Cranial cruciate ligament (CCL) rupture is one of the most important orthopedic diseases taught to veterinary undergraduates. The complexity of the anatomy of the canine stifle joint combined with the plethora of different surgical interventions available for the treatment of the disease means that undergraduate veterinary students often have a poor understanding of the pathophysiology and treatment of CCL rupture. We designed, developed, and tested a three dimensional (3D) animation to illustrate the pertinent clinical anatomy of the stifle joint, the effects of CCL rupture, and the mechanisms by which different surgical techniques can stabilize the joint with CCL rupture. When compared with a non-animated 3D presentation, students' short-term retention of functional anatomy improved although they could not impart a better explanation of how different surgical techniques worked. More students found the animation useful than those who viewed a comparable non-animated 3D presentation. Multiple peer-review testing is required to maximize the usefulness of 3D animations during development. Free and open access to such tools should improve student learning and client understanding through wide-spread uptake and use.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".