Body Painting of the Horse and Cow to Learn Surface Anatomy
Bibliographic record
Abstract
Gross anatomy is considered one of the most challenging subjects in teaching veterinary medicine. The use of body painting is reported in teaching surface human anatomy, but such reports are scarce in veterinary medicine. The aim of this study was to describe a practical session for teaching surface anatomy using body painting with second-semester students of veterinary medicine. Two practical sessions using live animals (equine and bovine) were offered with a focus on the locomotor and nervous systems and splanchnology. Students believed that the body painting sessions helped them to understand the localization of structures, promoting long-term retention and integration of knowledge, and to approach large animals with more self-confidence. Forty-nine students took three short theoretical and practical exams: a pre-test on splanchnology (Q1), an immediate post-test on splanchnology (Q2), and a post-test after 7 weeks on the locomotor and nervous systems (Q3). Correct answers for theoretical Q1 and Q2 were statistically different (2.04 and 3.11 out of 5, respectively; p < .001), and higher scores were found for Q3 compared with Q1 (2.49 and 1.02 out of 5, respectively). The most common error observed in practical Q1 was underestimation of the real size of organs such as lungs, rumen in cattle, and cecum in horses. The results showed that body painting sessions improved learning of anatomical concepts and could serve as a bridge between cadaver anatomy and living animal anatomy. More body painting sessions could be included in other semesters of the veterinary medicine curriculum to better integrate anatomy knowledge.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".