University of Saskatchewan Radiology Courseware (USRC): An Assessment of Its Utility for Teaching Diagnostic Imaging in the Medical School Curriculum
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it