Augmented Reality in the LINDSAY Virtual Human: Adding a new Dimension in Tablet-based Medical Education
Bibliographic record
Abstract
Imagine you are a medical student at the University of Calgary. As part of your training in anatomy, you are looking at a cadaver. However you are not able to understand the physiological aspects that take place within the body. The problem is the cadaver is essentially dead human tissue. Is there a way to expand your view, to bring this body back to life—in a virtual world? You take out your iPad and run the LINDSAY Atlas. The Atlas’ augmented reality (AR) feature merges the virtual world and the physical world, caught in real time by the built-in camera! You now begin to see the inner workings of a live human, superimposed on a cadaver; the beating heart inside a dead chest cavity, a cut on a lifeless arm being clotted, a diseased organ being healed by the body in front of your eyes. But you want more! You lift a finger and use gestures to interact with and explore different anatomical parts in 3D space. This increases your understanding of integrative physiology. Thus LINDSAY’s combination of AR and mobile touch technology enhances your medical training and prepares you for the practical new world of medicine.
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 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 | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".