Evaluation of the effectiveness of 3D vascular stereoscopic models in anatomy instruction for first year medical students
Bibliographic record
Abstract
The head and neck region is one of the most complex areas featured in the medical gross anatomy curriculum. The effectiveness of using three-dimensional (3D) models to teach anatomy is a topic of much discussion in medical education research. However, the use of 3D stereoscopic models of the head and neck circulation in anatomy education has not been previously studied in detail. This study investigated whether 3D stereoscopic models created from computed tomographic angiography (CTA) data were efficacious teaching tools for the head and neck vascular anatomy. The test subjects were first year medical students at the University of Mississippi Medical Center. The assessment tools included: anatomy knowledge tests (prelearning session knowledge test and postlearning session knowledge test), mental rotation tests (spatial ability; presession MRT and postsession MRT), and a satisfaction survey. Results were analyzed using a Wilcoxon rank-sum test and linear regression analysis. A total of 39 first year medical students participated in the study. The results indicated that all students who were exposed to the stereoscopic 3D vascular models in 3D learning sessions increased their ability to correctly identify the head and neck vascular anatomy. Most importantly, for students with low-spatial ability, 3D learning sessions improved postsession knowledge scores to a level comparable to that demonstrated by students with high-spatial ability indicating that the use of 3D stereoscopic models may be particularly valuable to these students with low-spatial ability. Anat Sci Educ 10: 34-45. © 2016 American Association of Anatomists.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".