Development of a Web-Based 3D Module for Enhanced Neuroanatomy Education.
Bibliographic record
Abstract
Neuroanatomy is a challenging subject, with novice medical students often experiencing difficulty grasping the intricate 3D spatial relationships. Most of the anatomical teaching in undergraduate medicine utilizes conventional 2D resources. E-learning technologies facilitate the development of learner-centered educational tools that can be tailored to meet each student's educational needs, and may foster improved learning in neuroanatomy, however this has yet to be examined fully in the literature. An interactive 3D e-learning module was developed to complement gross anatomy laboratory instruction. Incorporating such 3D modules may provide additional support for students in areas of anatomy that are spatially challenging, such as neuroanatomy. Specific anatomical structures and their relative spatial positions to other structures can be clearly defined in the 3D virtual environment from viewpoints that may not readily be available using cadaveric or 2D image modalities. Providing an interactive user interface for the 3D module in which the student controls many factors may enable the student to develop an improved understanding of the spatial relationships. This work outlines the process for the development of a 3D interactive module of the cerebral structures included in the anatomy curriculum for undergraduate medical students in their second year of study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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".