Reimagining dissection lab preparation ‐ the role of digital media in anatomy education
Bibliographic record
Abstract
Introduction Learning materials provided to students prior to anatomy dissection labs have classically been in the form of written instructions and images from prosections. However, these materials can be difficult to interpret, especially for beginner students. With the advancement of technology, educational curricula are exploring the integration of digital platforms to supplement more traditional teaching methods. Goal To investigate the utility of a video‐based guide for approaching dissection. Methods A video dissection guide was created demonstrating the dissection of the superficial back. The video outlines the anatomy, technique, procedure, and includes review questions. The video was made available online through YouTube, along with a feedback survey. A secondary video dissection guide highlighting the dissection of the deep back was subsequently produced and made available in the same avenue. Results After two months, the superficial back dissection video has received more than 1,400 views. Feedback was received from 68 respondents; a majority of whom were female (63.2%), aged 20–24 (60.3%), and had a current education level of a postgraduate degree or professional degree (54.4%). The majority of respondents agree or strongly agree that the video presented the anatomy in a clear and organized fashion (95.6%), enhanced their learning of the anatomy (100%), familiarized them with dissection tools and how to use them (89.7%), familiarized them with methods and techniques for dissection (92.6%), was more effective than a dissection guide (97.1%), and was more effective for learning anatomy than a textbook (88.2%). A poll of 54 respondents found that most agree or strongly agree that the video is a valuable resource for review/test preparation (88.9%). Preliminary survey results for the deep back dissection video indicates that, of 15 respondents, the majority agree or strongly agree that the video presented the anatomy in a clear and organized fashion (100%), enhanced their learning of the anatomy (93.3%), was more effective than a dissection guide (93.3%), and is a valuable resource for initial learning of dissection/anatomy (100%) and review/test preparation (86.7%). Conclusion In summary, digital media in the form of video‐based dissection guides may be a useful tool to incorporate into educational curricula for teaching gross anatomy of both superficial and deep structures. Future goals include the incorporation of clinically relevant information to dissected anatomical structures.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 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".