One-week radiology boot camp for pre-clerkship medical students: A novel format improving image interpretation and confidence
Bibliographic record
Abstract
RATIONALE AND OBJECTIVES: To measure change in radiology knowledge, confidence in radiology skills, and perceptions pertaining to radiology following a one-week boot camp elective for undergraduate medical students. MATERIALS AND METHODS: A five-day comprehensive radiology boot camp was developed including sessions on image interpretation, procedural skills, and appropriate image ordering. A multiple-choice quiz was administered before and after the elective, utilizing radiology questions from the validated AMSER STARS database. Additionally, a pre- and post-elective survey was administered assessing radiology career interest, confidence in radiology-based skills, and the potential ability of radiology-based skills to increase confidence in specialties other than radiology. Responses from the assessments were analysed using paired t-tests. RESULTS: 15 students enrolled in the course and 14 completed all assessments. The average score on the quiz increased from 50.1% to 66.0% (p<0.001). On the post-elective survey, the average student confidence score increased by more than one point on a six-point Likert scale in each of radiographic interpretation (p=0.004), ultrasound interpretation (p=0.0002), CT/MRI interpretation (p=0.02), general radiology knowledge including procedural skills (p=0.0001), and appropriate image ordering (p=0.004). Average student satisfaction with the elective was 8.1 out of 10. CONCLUSION: A one-week radiology boot camp for pre-clerkship medical students improved radiology knowledge and confidence in radiology skills, showing potential for this format to meet the demand for increased radiology content in undergraduate training. Students indicated that confidence in radiology knowledge would increase confidence on non-radiology clerkship rotations, highlighting the importance of how a one-week radiology bootcamp can impact both future radiology and non-radiology clerkship experiences.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".