The integration of anatomy and radiology through interactive online modules
Bibliographic record
Abstract
Medical undergraduate programs require anatomy to be taught in a clinically relevant context. One natural fit for anatomy teaching is to integrate it with radiology. For this purpose anatomists and radiologists at UBC developed an integrated modular course that is tightly correlated with the anatomy curriculum, incorporating applicable anatomical structures with clinical radiology. We have created interactive modules and quizzes that correlate radiological anatomy to the relevant gross anatomy, and provide basic clinical applications. The regions covered include: spine, shoulder and elbow, thorax, abdomen, pelvis and head. Students are encouraged to participate interactively with mix and match, fill in the blank, and labeling activities. Feedback from the students allows faculty to assess which areas of the current integration process are less effectual and demonstrate avenues for potential improvement to the curriculum. Currently this project is focused on the first year of the UBC curriculum, but the format lends itself to expansion across all four years as well as continuing integration through the postgraduate years. Future anatomy teaching will likely move away from dedicated blocks of time and towards an approach integrated with clinical disciplines. Integration with the radiology program demonstrates the effectiveness of comprehensive anatomy teaching as it relates to a clinical discipline. Grant Funding Source : N/A
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.018 |
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".