Development of a computer‐assisted cranial nerve simulation from the visible human dataset
Bibliographic record
Abstract
Advancements in technology and personal computing have allowed for the development of novel teaching modalities such as online web-based modules. These modules are currently being incorporated into medical curricula and, in some paradigms, have been shown to be superior to classroom instruction. We believe that these modules have the potential of significantly enriching anatomy education by helping students better appreciate spatial relationships, especially in areas of the body with greater anatomical complexity. Our objective was to develop an online module designed to teach the anatomy and function of the cranial nerves. A three-dimensional model of the skull, brainstem, and thalamus were reconstructed using data from the Visible Human Project and Amira®. The paths of the cranial nerves were overlaid onto this 3D reconstruction. Videos depicting these paths were then rendered using a "roller coaster-styled" camera approach. Interactive elements adding textual information and user control were inserted into the video using Adobe Creative Suite® 4, and finally, the module was exported as an Adobe Flash movie to be viewable on Internet browsers. Fourteen Flash-based modules were created in total. The primary user interface comprises a website encoded in HTML/CSS and contains links to each of the 14 Flash modules as well as a user tutorial.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".