University of Saskatchewan Radiology Courseware (USRC): An Assessment of Its Utility for Teaching Diagnostic Imaging in the Medical School Curriculum
Bibliographic record
Abstract
PROBLEM: We have found it very challenging to integrate images from our radiology digital imaging repository into the curriculum of our local medical school. Thus, it has been difficult to convey important knowledge related to viewing and interpreting diagnostic radiology images. We sought to determine if we could create a solution for this problem and evaluate whether students exposed to this solution were able to learn imaging concepts pertinent to medical practice. INTERVENTION: We developed University of Saskatchewan Radiology Courseware (USRC), a novel interactive web application that enables preclinical medical students to acquire image interpretation skills fundamental to clinical practice. This web application reformats content stored in Medical Imaging Resource Center teaching cases for BlackBoard Learn™, a popular learning management system. We have deployed this solution for 2 successive years in a 1st-year basic sciences medical school course at the College of Medicine, University of Saskatchewan. The "courseware" content covers both normal anatomy and common clinical pathologies in five distinct modules. We created two cohorts of learners consisting of an intervention cohort of students who had used USRC for their 1st academic year, whereas the nonintervention cohort was students who had not been exposed to this learning opportunity. CONTEXT: To assess the learning experience of the users we designed an online questionnaire and image review quiz delivered to both of the student groups. OUTCOME: Comparisons between the groups revealed statistically significant differences in both confidence with image interpretation and the ability to answer knowledge-based questions. Students were satisfied with the overall usability, functions, and capabilities of USRC. LESSONS LEARNED: USRC is an innovative technology that provides integration between Medical Imaging Resource Center, a teaching solution used in radiology, and a Learning Management System.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".