Plastination in Anatomy Learning: An Experience at Cambridge University
Bibliographic record
Abstract
Due to lack of objective data, the benefits of using plastination in combination with wet dissection in teaching gross anatomy are unknown. The aim of this study was to obtain objective evidence from students regarding the effectiveness of combining plastinated specimens (PS) with an established gross anatomy education program at Cambridge University that uses wet cadaver dissection and small-group tutorials. For a complete academic year, a total of 135 PS were used alongside wet cadaver dissections. The PS were also available for small-group tutorials. An anonymous closed questionnaire, using a 5-point numerical-estimation Likert scale, was used to gather information relating to the effectiveness of the PS. The level of student satisfaction with the combined use of wet dissections and PS was high, although higher (p<.05) for second-year students (98.4%) than for first-year students (95.5%). Students felt the specimens allowed them to see details that were often more difficult to identify in their dissections, for instance nerves. Voluntary use of PS was higher (p<.01) for second-year students (96.9%), who had previously experienced anatomy teaching with cadaver dissection alone, than for first-year students (77.7%). Overall, 97.7% of all students thought that the PS helped them understand and learn anatomy. All students surveyed (100%) recommended the use of PS in the future. Students considered the use of PS in the dissection room combined with wet cadaver dissection to be beneficial when learning anatomy, particularly when combined with their use during small-group tutorials.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".