The role of 3D printing in teaching and education in human skeletal anatomy
Bibliographic record
Abstract
The project represents a partnership between physical anthropology and human anatomy for the creation of 3D anatomical models for research and training. The models were created from post‐mortem X‐Ray CT scans of teaching cadavers. Initial steps involved identifying tissues, structures or regions of interest, and segmenting the structures of interest. Once a 3D model was rendered, further editing and refinement was required for 3D printing. CT scans were undertaken at the Health Sciences Centre, University of Manitoba. Analysis of the data was undertaken in the Bioanthropology Digital Image Analysis Laboratory (BDIAL), University of Manitoba. The data were edited and rendered using Materialise MIMICS and INUS Rapidform software. Physical models were created using a Z‐Corp Z406 3D printer. Preliminary input from students demonstrates the impact of the models for training and research, particularly the hand‐on nature of viewing models. Of particular benefit is the ability to reveal hidden structures at increased magnification, facilitating better understanding of the anatomical relationship of structures not easily visible in cadavers or photos. The use of 3D printing provides an innovative, on‐demand, pedagogical tool benefiting a variety of training needs, including physical anthropology, clinical and basic medical sciences. Funded by University of Manitoba; Canada Research Chairs Program.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".