How Useful Is Plastination in Learning Anatomy?
Bibliographic record
Abstract
In recent years plastination has begun to revolutionize the way in which human and veterinary gross anatomy can be presented to students. The study reported here assessed the efficacy of plastinated organs as teaching resources in an innovative anatomy teaching/learning system. The main objective was to evaluate whether the use of plastinated organs improves the quality of teaching and learning of anatomy. For this purpose, we used an interdepartmental approach involving the departments of Veterinary Anatomy, Human Anatomy, Veterinary Surgery, and Education Development and Research Methods. The knowledge base of control and experimental student groups was examined before and after use of the fixed or plastinated resources, respectively, to gather information evaluating the effectiveness of these teaching resources. Significant differences (p < 0.001) between control and experimental groups of Human and Veterinary Anatomy were observed in the post-test results. The Veterinary Surgery students had the most positive opinion of the use of plastinated specimens. Using these data, we were able to quantitatively characterize the use of plastinated specimens as anatomy teaching resources. This analysis showed that all the plastinated resources available were heavily used and deemed useful by students. Although the properties of plastinated specimens accommodate student needs at various levels, traditional material should be used in conjunction with plastinated resources.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".