Development of a 3D Printed Neuroanatomy Teaching Model
Bibliographic record
Abstract
Gross anatomy has been seen as one of the basic bodies of knowledge that must be mastered as part of medical training. Likewise, neuroanatomy has been seen as foundational to clinical neurosciences. However, Neuroanatomy is different from gross anatomy and this is due to the complexity of the central nervous system, moreover, some of its structures cannot be dissected or demonstrated in anatomy cadaveric lab. The use of anatomical models in medical curricula has been reported as an effective way in teaching and learning anatomy. They have been used to replace cadaveric material when the latter is difficult to acquire, or when the anatomical structures cannot be dissected like the brain ventricles for instance, moreover they have the privilege of visualizing the structures in a 3 dimensional modality. The goal of this study was to create a 3 D printed neuroanatomy model in order to complement the University of Ottawa anatomy models’ library, and help medical students visualize the pathway of different nervous tracts on a 3 D simulation model.To assist with this, 2D images of slices of the cerebrum, brainstem, cervical, thoracic, and lumbar spinal cord were downloaded online to be imported to Adobe Photoshop CC 2015. The images were manually converted to black and white, and separated into different layers to export each components separately into Tinker CAD (online software). The different components were then assembled on Tinker CAD to create 3D printer compatible files. The files were printed using white ABS on a Replicator 2X MakerBot printer at the library of University of Ottawa.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.012 | 0.004 |
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".