Size Matters! Evaluating the Effect of Model Size on Anatomy Learning
Bibliographic record
Abstract
Historically, learning anatomical specimens was limited to studying cadaveric materials, and by extension, specimens which are “life‐sized”. Recent technological advancements in 3D scanning and printing now allow for the production of inexpensive, durable anatomical replicas at virtually any size. This, however, creates a dilemma: what is the most effective model size to learn from? The goal of this project is to discover the appropriate size of an object to learn nominal anatomy and thus provide a critical step in improving anatomic education. We hypothesize that there is a curvilinear relationship between model size and learning, where a model too small or too big would not be the most conducive to learning and an ideal intermediate size can be determined. In this study, undergraduate students (n = 351) without prior anatomical training learned from four bones of varying normal anatomical size and features and were assessed on their ability to identify various landmarks. Thoracic vertebra (VE), hemipelvis (HE), sphenoid (SP), and scapula (SC) was 3D‐printed at four different scalar sizes. The VE and HE models were printed at 50%, 100%, 200%, and 400% scale, while SP and SC models were printed at 50%, 100%, 200%, and 300% scale. Each participant was randomly assigned to a group of two bone models (VE/HE or SP/SC) of a certain size, and randomized across the order in which they learned the models. They were then tested on the respective real bone specimens, followed by a qualitative survey reporting their experience with the 3D‐printed models, a Mental Rotations Test (MRT), and an Operation Span Test (OSPAN). Data collection for the 50% SP/SC group is still ongoing. Multiple regression suggested significant effects of Model Type, Model Size, MRT and OSPAN, (F(9, 596) = 17.96, p = 0.000, R2 = 0.2133). The most significant predictor of test score was MRT, which suggested a 10% increase in MRT score is associated with a ~3% increase in test score. The score variability independently accounted for by MRT and OSPAN was 14.6%, while the variability independently accounted for by model size and type was 7.8%. This means, that while test scores are primarily driven by participants’ mental rotation ability, model size remains an important feature that can be manipulated to improve learning. 3D printing allows for this in a cost‐effective way. Support or Funding Information This study was funded by the Education Program in Anatomy at McMaster University. Many thanks to the University of Buffalo for 3D printing the 400% VE and HE models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".