Exploring the Utility and Student Perceptions of Synthetic Cadavers in an Undergraduate Human Anatomy Course
Bibliographic record
Abstract
The synthetic cadaver is a high-fidelity model intended to replace or supplement other anatomy learning modalities. Academic attainment and student perceptions were examined in an undergraduate human anatomy course using a combination of plastic models and synthetic cadavers to learn lower body anatomy ("Experimental group"), compared to a Historical group who used only plastic models. Grades on an upper body test, for which both groups used only plastic models, were compared to ensure that no academic differences existed between groups (P = 0.7653). Students in the Experimental group performed better on the lower body test for which they used both plastic models and synthetic cadavers (median = 73.8% (95% CI: 72.0%-75.0%) compared to the Historical group (70.1% (95% CI: 68.3%-70.7%), P < 0.0001); however, less than half of students (49%) attributed this to the synthetic cadavers. Students' perception of laboratory resources (P < 0.0001) and learning experience (P < 0.0001) both improved with the addition of synthetic cadavers compared to using only plastic models, and 60% of students in the Experimental group agreed that the synthetic cadavers would be a key reason that they would choose that institution for undergraduate studies. This investigation showed improved student grades when plastic models and synthetic cadavers were combined, in addition to improved student perceptions of the learning experience. Results of the student questionnaires also suggested that although synthetic cadavers carry a notable up-front cost, they may be a useful recruitment tool for institutions.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".