Physical Models Dominate: The Pirate Patch Study
Bibliographic record
Abstract
Prior studies have shown physical models are superior learning tools compared to interactive‐two‐dimensional models (3D images on 2D surfaces) and key views of the specimen when tested on a cadaver. Additionally, we have shown that haptic feedback and transfer‐appropriate processing do not contribute to the superiority of the physical model. In the current study, we explored the role of stereopsis in the same context. During the learning phase, we compared a condition with both eyes uncovered to a condition with the non‐dominant eye covered, removing stereopsis. The results further validate that physical models are superior to the interactive 2D model. Participants in the physical model group performed significantly better than those in the interactive 2D group on structure identification on the cadaveric pelvis (64% vs 47%, p < 0.001). Furthermore, the use of two eyes was superior to one eye (62% vs 49%, p < 0.01). However, there were no interactions, suggesting that stereopsis had no differential effect in the 2D model. Ultimately, these results further support the superiority of the physical model for anatomy education.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 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".