Teaching Radiology to Medical Students in Canada; a Virtual, Integrative, Clinical Approach
Bibliographic record
Abstract
PURPOSE: To construct, apply, and evaluate a multidisciplinary approach in teaching radiology to Canadian medical students. METHODS: A multidisciplinary team of radiology and other disciplines experts designed an online 5-session course that was delivered to medical students. The topics of each session were clinical cases involving different systems. The target audience was medical students of Canadian schools. Pretests and post-tests were administered before and after each session respectively. An evaluation survey was distributed at the end of the course to gauge students' perceptions of this experience. RESULTS: An average of 425 medical students attended the live sessions. For each session, 405 students completed both the pre-tests and post-tests. In general, students scored an average of 56% higher on the post-test than on the pre-test. The final course survey was completed by 469 students. The survey results show that more than 98% of students found the course to meet or exceed their expectations. Over 80% of students agreed that the course increased their interest in radiology and about 81% agree that the topics presented were excellent and clinically important. The ratings in the final survey results also indicate that students increased their confidence in basic radiology skills after completing the course. CONCLUSIONS: The implementation of an integrative clinical approach to teaching radiology in a virtual setting is achievable. It provides efficient use of educational resources while being accessible by a large number of students across different 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".