Plastinated Prosections and Nomenclature Charts Are Valuable Supplementary Learning Resources for Veterinary Anatomy Students in Dissection Classes and for Self-Study
Bibliographic record
Abstract
Anatomy is a central pillar of veterinary education, and it is an ongoing goal to optimize teaching methods so that students are well prepared for their future practice. In the present study, we investigated whether plastinated prosections could serve as valuable supplementary learning tools during organized dissection courses and for self-studies. To enable independent student use, we also created nomenclature charts describing the anatomical structures on the prosections. Our study involved 89 veterinary students in their third semester of veterinary education, studying organ-based anatomy. The teaching intervention took place during four dissection classes, where all students dissected formalin-fixed dog cadavers, and where half of the students had access to additional in-house plastinated prosections with associated nomenclature charts, while the remaining students did not. After each dissection class, the students were given an immediate knowledge test and were asked about their perceived learning benefits. Subsequently, the plastinated prosections and nomenclature charts were available for all students for self-study for exam preparation, in addition to digital access to the nomenclature charts. Our results showed that the students frequently used the learning supplements and expressed high satisfaction with the plastinated prosections and the nomenclature charts but did not perform significantly better on the knowledge tests. A post-exam survey revealed that the plastinated prosections and nomenclature charts were among the top three most frequently used learning resources for the exam. In conclusion, plastinated prosections and associated nomenclature charts are valuable learning supplements in veterinary anatomy education, both during organized dissection courses and for self-studies.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".