Evaluation of the effectiveness of 3D vascular stereoscopic models in anatomy instruction for first year medical students
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it