The Promise of Mixed Reality in Anatomy Education
Bibliographic record
Abstract
We have previously demonstrated that when anatomy is learned from traditional 3D computer models (i.e., those projected on flat screens) or using pictures and diagrams of specimens, test scores are approximately 30% less than when the anatomy is learned from solid models when students are tested on cadavers. We have shown that these traditional, computer generated 3D images are perceived as 2D images due to our use of 2D‐displays. Our current goal is to utilize technology that allow our learners to perceive these computer objects as true 3D models i.e., as solid objects. Recent developments in technology, like the Microsoft HoloLens, allow us to generate convincing, interactive, 3D models in real space. These mixed‐reality anatomic objects have the potential to be as efficient as our solid models in learning anatomy and thus may replace the traditional tools of anatomic teaching. This presentation will demonstrate anatomic specimens in all formats, including mixed reality applications in an interactive setting to emphasize the benefits and problems of each form of learning object. Support or Funding Information Education Services, Faculty of Health Sciences and MacPherson Institute, McMaster University.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".