Development of an Online Distance Learning Platform Combining Anatomy, Imaging, and Surgical Practice to Support Mastery Learning of the Equine Locomotor Apparatus
Bibliographic record
Abstract
There are many challenges in teaching veterinary anatomy, such as available classroom time, costs, and difficulties accessing animal cadavers, mainly due to animal welfare concerns. Furthermore, veterinary surgeons and radiologists complain that recent graduates lack anatomical knowledge. On the other hand, the current limitations of face-to-face teaching due to the COVID-19 pandemic suggest that the development of online distance education tools is necessary, mainly in specialties that lack this type of material. Teaching platforms promoting the integration of anatomy with other applied disciplines such as imaging and surgery in the horse were not found in the consulted literature. Therefore, this work aimed to develop an online distance education platform for studying the surgical anatomy of a horse's locomotor apparatus as a complementary tool for training students enrolled in undergraduate courses in veterinary surgery. The locomotor apparatus was chosen as the focus as it is the most commonly found in equine surgeries. Anatomical pieces referring to the locomotor apparatus were prepared. These were complemented with material related to diagnostic imaging, surgery videos, theoretical explanations, and an interactive radiological anatomy tool. Finally, all the material was uploaded to a virtual platform accessible via the Internet. The platform is expected to be a tool that helps students in surgical training and prepares them with a better understanding of anatomy and its application in surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".