An authentic link to the clinic: Implementation of an integrated anatomy‐radiology curriculum
Bibliographic record
Abstract
Since 2011, the University of British Columbia (UBC) medical curriculum has taught anatomy in conjunction with radiology in order to give students a greater understanding of anatomical relationships and their application to clinical practice. Based on student preference, we are preparing a learning resource which encompass highly‐interactive computed tomography (CT) studies using an anatomy visualization table (Sectra, Linkoping, Sweden). Real patient CT scans are loaded into an anatomy visualization table and students can virtually cut and dissect these datasets to expose the anatomy in two and three dimensions. We would like to present a model for the integration of digital dissection with cadaveric dissection in a first year curriculum of a large medical school, with the aim of providing a direct link from the lab to the clinical application. Methods Normal CT studies of the spine, thorax, heart, abdomen and pelvis were identified from the online case library affiliated with the anatomy visualization table and labeled to reflect specific learning objectives for first year UBC medical students. The radiological and gross anatomy objectives are closely linked. The students were provided with lab guides that specify UBC‐labeled files for study, learning objectives and relevant anatomy/clinical review questions pertinent to each lab. Students viewed both the two and three dimensional images during the lab. Results This anatomy visualization table is a highly valuable learning tool as it allows students to better visualize and relate anatomical structures they learn in dissection lab with the same structures on two dimensional and three dimensional CT images. The table provides improved appreciation for anatomical relationships by allowing students to zoom, rotate and dissect 3D CT images with simple swiping hand gestures. This abstract will present lessons learned from this integration including the highlights and the challenges. Conclusions For medical students, who will be primarily looking at anatomy on radiology images throughout their careers, this technology allows them to become more familiar with radiological imaging early on in their training. Our radiological anatomy‐gross anatomy integrated curriculum allows students to link the clinical application of the anatomy to their dissection experience, thereby providing an authentic link to the clinic where they will be applying their knowledge throughout their careers.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.009 |
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".