An interactive 3D model of the cranial nerve and brainstem nuclei for enhanced learning of neuroanatomy
Bibliographic record
Abstract
Neuroanatomy is a complex sub‐discipline of anatomy that requires abstract thinking and strong spatial reasoning. Traditional methods of learning include dissection, diagrams, and histology. This pedagogical approach requires students to formulate three‐dimensional (3D) mental images from two‐dimensional (2D) cross‐sections. Previous studies demonstrate students with lower spatial abilities have difficulty learning the anatomy of the brainstem nuclei partly due to their inability to conceptualize topography. The purpose of this study was to design and implement a 3D model of the cranial nerve and brainstem nuclei into an online learning tool that highlights their spatial relationships. The second purpose was to test the learning tool against traditional methods. This tool was compared to a classical approach using a randomized, cross‐over design. It is hypothesized that while subject to the same learning objectives, students learning with the 3D tool demonstrate enhanced knowledge of the spatial relationships of the nuclei compared to the students who learned through the classical approach. A standardized test and an open‐ended questionnaire were used to measure efficacy and student preferences. Information from this study will help guide the formation of new e‐learning tools that are becoming pervasive in anatomical sciences. Grant Funding Source : none
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".