The Utilisation of Radiology for the Teaching of Anatomy in Canadian Medical Schools
Bibliographic record
Abstract
OBJECTIVE: To determine the utilisation of diagnostic imaging (radiology) as a department and/or imaging medium in the teaching of anatomy at the Canadian undergraduate medical education level. METHODS: The study objectives were achieved through the use of a questionnaire and a literature review. The anatomy department head at each English-based Canadian Medical School was contacted, and the individual most responsible for anatomy teaching in the medical school curriculum was identified. This individual was subsequently asked to complete a questionnaire that evaluated the involvement of radiology for anatomy teaching in their curriculum. RESULTS: The use and integration of radiology is a common practice in the teaching of anatomy in Canadian undergraduate medicine. Although the methods and extent of its use varied among institutions, every English-based Canadian medical school, except one, was using diagnostic imaging material in their instruction of anatomy. Furthermore, half of the institutions had a radiologist as a faculty member of their anatomy department to help teach and to use imaging to its full potential. DISCUSSION: This audit of anatomy departments suggests that diagnostic imaging has an important role to play in anatomy teaching in Canadian English-speaking medical schools.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".