A head in virtual reality: Development of a dynamic head and neck model
Bibliographic record
Abstract
Advances in computer and interface technologies have made it possible to create three-dimensional (3D) computerized models of anatomical structures for visualization, manipulation, and interaction in a virtual 3D environment. In the past few decades, a multitude of digital models have been developed to facilitate complex spatial learning of the human body. However, there is limited empirical evidence to guide the development and integration of effective computer models for teaching and learning. The purpose of this article is to describe the development of a dynamic head and neck model with flexible displays (2D, 3D, and stereoscopic 3D) and interactive control features that can be later used to design and test the efficacy of computer models as a means of improving student learning. The model was created using computer tomography scans of a human cadaver. Anatomical structures captured on the scans were segmented into discreet areas, and then reconstructed in three-dimensions using specialized software. The final model consists of 70 distinct anatomical structures that can be displayed in 2D, 3D, or stereoscopic 3D. In 3D mode, a mouse can be used to actively and continuously interact with the model by manipulating viewer orientation, altering surface transparency, superimposing 2D scans with 3D reconstructions, removing or adding structures sequentially, and customizing animated scenes to show complex anatomical pathways or relationships.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".