An Internet-Based Radiology Course in Medical School: Comparison of Academic Performance of Students on Campus Versus Those With Absenteeism Due to Residency Interviews
Bibliographic record
Abstract
BACKGROUND: Imaging and its optimal use are imperative to the practice of medicine, yet many students don't receive a formal education in radiology. Concurrently, students look for ways to take time away from medical school for residency interviewing. Web-based instruction provides an opportunity to combine these imperatives using online modalities. OBJECTIVE: A largely Web-based course in radiology during the 4th year of medical school was evaluated both for its acceptance to students who needed to be away from campus for interviews, and its effectiveness on a nationally administered standardized test. METHODS: All students were placed into a structured program utilizing online videos, online modules, online textbook assignments, and live interactive online lectures. Over half of the course could be completed away from campus. The Alliance of Medical Student Educators in Radiology test exam bank was used as a final exam to evaluate medical knowledge. RESULTS: Positive student feedback included the freedom to travel for interviews, hands-on ultrasound training, interactive teaching sessions, and quality Web-based learning modules. Negative feedback included taking quizzes in-person, a perceived outdated online textbook, and physically shadowing hospital technicians. Most students elected to take the course during the interview months of October through January. The Alliance of Medical Student Educators in Radiology final exam results (70.5%) were not significantly different than the national cohort (70%) who took the course in-person. Test scores from students taking the course during interview travel months were not significantly different from students who took the course before (P=.30) or after (P=.34) the interview season. CONCLUSIONS: Students desire to learn radiology and often choose to do so when they need to be away from campus during the fall of their 4th year of study to accomplish their residency interviews. Web-based education in radiology allows students' interview traveling and radiology course objectives to be successfully met without adversely affecting the outcomes on a nationally normed examination in radiology. A curriculum that includes online content and live Web-based teleconference access to faculty can accomplish both imperatives.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".