Size matters! Investigating the effects of model size on anatomy learning
Bibliographic record
Abstract
Abstract Three‐dimensional (3D) scanning and printing technology has allowed for the production of anatomical replicas at virtually any size. But what size optimizes the educational potential of 3D printing models? This study systematically investigates the effect of model size on nominal anatomy learning. The study population of 380 undergraduate students, without prior anatomical knowledge, were randomized to learn from two of four bone models (either vertebra and pelvic bone [os coxae], or scapula and sphenoid bone), each model 3D printed at 50%, 100%, 200%, and either 300% or 400% of normal size. Participants were then tested on nominal anatomy recall on the respective bone specimens. Mental rotation ability and working memory were also assessed, and opinions regarding learning with the various models were solicited. The diameter of the rotational bounding sphere for the object (“longest diameter”) had a small, but significant effect on test score ( F (2,707) = 17.15, p < 0.05, R 2 = 0.046). Participants who studied from models with a longest diameter greater than 10 cm scored significantly better than those who used models less than 10 cm, with the exception of the scapula model, on which performance was equivalent across all sizes. These results suggest that models with a longest diameter beyond 10 cm are unlikely to incur a greater size‐related benefit in learning nominal anatomy. Qualitative feedback suggests that there also appear to be inherent features of bones besides longest diameter that facilitate learning.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".