Stereoscopic (3D) Visualization Improves Medical Student Comprehension of Head and Neck Vascular Anatomy
Bibliographic record
Abstract
The use of 3D stereoscopic models (3DSM) of head and neck blood vessels in anatomy education has not been studied in detail. We investigated whether 3DSM created from a prior study (Cui et al. 2015) would improve student learning of head and neck vascular anatomy. Comparisons were made between the use of 3DSM and identical but 2D flat screen images extracted from the 3D models in a first year medical gross anatomy course. Anatomical knowledge was tested via pre‐ and post‐learning session anatomy knowledge tests. In addition student fluency with mental rotation (pre‐ and post‐session rotation tests.) 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. Baseline pre‐learning session test scores were equivalent, 5.85±2.37 and 5.03±1.93 for 3DSM and 2D groups respectively. Students who utilized the 3DSM (n=21) scored significantly higher on the post‐learning tests compared to those using 2D images (n = 18) (11.43±2.79 vs 8.75±2.81) (p =0.0033). There was no significant difference on the mental rotation test scores between 3DSM (17.29±5.36) and 2D groups (17.39±6.42) after learning sessions (p=0.6208). In summary, the use of virtual stereoscopic 3D models improved medical student performance on knowledge tests of head and neck vascular anatomy, suggest new avenues for the use of virtual models in medical education. Support or Funding Information Faculty Scholarship Exchange Award
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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 | 0.000 | 0.002 |
| 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) | 0.007 | 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".