Cadaveric Prosections Prepared by Qualified Instructional Staff Were More Efficient and Effective Teaching Modalities for Veterinary Gross Anatomy than In-Class Dissections by Students
Bibliographic record
Abstract
Veterinary programs traditionally teach gross anatomy by having students perform regional dissections on animal cadavers. Dissection is effective but also costly, time consuming, and intimidating for students. These factors, along with reduced contact hours devoted to gross anatomy, warrant investigation of more time-efficient teaching modalities. We sought to determine whether learning anatomy from instructor-prosected cadavers is a suitable alternative to in-class cadaveric dissections. Veterinary students completed nine units of regional gross anatomy over three courses. For each unit, students were randomly assigned to study the region on instructor-prosected cadavers (i.e., prosection students, n = 25) or perform their own dissection of the region in small groups (i.e., dissection students, n = 25). Prosection students spent on average 18 minutes/week less ( p < .05) in class than dissection students. Despite comparable amounts of time spent studying outside of class each week, prosection students outperformed ( p < .05) dissection students on 56% of the practical unit exams and 44% of the overall unit exams, whereas dissection students outperformed ( p < .05) prosection students on only a single unit exam. Prosection students also performed better ( p < .05) on subsequent quizzes administered to assess knowledge retention. Survey responses indicated that students were more confident in the accuracy of prosections and valued the efficiency they provided. Although they found value in performing dissections and were generally satisfied with the knowledge they gained, many students reported feeling timid toward dissecting, which diminished the experience. Together, these findings demonstrate that expertly prosected cadavers were more time-efficient than in-class cadaveric dissections and were generally more effective for learning gross veterinary anatomy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".