Senior medical students’ awareness of radiation risks from common diagnostic imaging examinations
Bibliographic record
Abstract
BACKGROUND: Senior medical students represent future physicians who commonly refer patients for diagnostic imaging studies that may involve ionizing radiation. The radiology curriculum at the University of British Columbia provides students with broad-based knowledge about common imaging examinations. The purpose of this study was to investigate students' awareness of radiation exposures and risks. METHODS: An anonymous multiple-choice cross-sectional questionnaire was distributed to final year medical students to assess knowledge of radiation from common diagnostic examinations and radiation-related risks following completion of the longitudinal radiology curriculum, carried out over the four years of medical training. RESULTS: Sixty-three of 192 eligible students participated (33% response rate). The majority felt that knowledge of radiation doses of common imaging examinations is somewhat or very important; however, only 12% (N = 8) routinely discuss radiation-related risks with patients. While all respondents recognized children as most sensitive to the effects of radiation, only 24% (N = 15) correctly identified gonads as the most radiation-sensitive tissue. Almost all respondents recognized ultrasound and MRI as radiation free modalities. Respondents who correctly identified the relative dose of common imaging examinations in chest x-ray equivalents varied from 3-77% (N = 2 - 49); the remaining responses were largely underestimates. Finally, 44% (N = 28) correctly identified the excess risk of a fatal cancer from an abdominal CT in an adult, while the remainder underestimated this risk. CONCLUSION: Medical students acknowledge the importance of radiation-related issues to patient care. While almost all students are familiar with radiation-free modalities, many are not familiar with, and commonly underestimate, the relative doses and risks of common imaging studies. This may expose patients to increasing imaging investigations and exposure to radiation hazards.
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How this classification was reachedexpand
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 | 0.001 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".