3D‐X: making spatial sense of cross‐sectional anatomy in an online learning environment
Bibliographic record
Abstract
Residency programs are demanding an increased knowledge of cross‐sectional anatomy from entering physicians, while medical schools are decreasing time allotted to teaching anatomy. The increasing use of e‐learning resources in medical education led us to create a web‐based tool to facilitate the learning of cross‐sectional anatomy. A 3D model of the abdomen was constructed in Amira 5.0, by segmenting 600 images from the Visible Male cryo‐section data. A number of cross‐sectional images were imported into Flash CS4 along with five reconstructed models, and interactive controls for the 3D models and dynamic labels for the cross‐sections were programmed using the ActionScript 3.0 engine in CS4. The application, titled 3D‐X, was exported in a web‐friendly flash format and published online. In 3D‐X, users can view seven key cross‐sections, while the planes of origin are indicated on the 3D model. The 3D model can be rotated and modified to view various structures from different angles. Moving the cursor over an anatomical structure on a 2D cross‐section causes the structure's textual label to appear and colors the structure to match its counterpart on the 3D model. Clicking a structure on a 2D cross‐section modifies the 3D model to create an unobstructed view of the structure and may help users correlate 2D and 3D information. Based on a research study, 3D‐X co‐administered with cadaveric anatomy improved CT interpretation when compared with learning solely through cadaveric anatomy. 3D‐X therefore shows promise as a tool for enhanced learning of cross‐sectional anatomy by providing a familiar 3D context to aid with spatial orientation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.063 | 0.014 |
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".