Medical radiation knowledge among medical doctors and students in Iran: a nationwide cross-sectional study
Bibliographic record
Abstract
Abstract Introduction Radiology plays an important role in medical diagnoses, and proper techniques are critical in disease diagnosis. As a result, physicians’ and medical students’ knowledge of this field, as well as their mastery of imaging techniques, is critical. The purpose of this study was to assess Iranian medical students, residents, and physicians’ knowledge of the fundamentals of various imaging methods, the amount of ionizing radiation in each type of imaging, and diagnostic imaging techniques. Methods This is a cross-sectional study in which 454 general practitioners, residents and medical interns in Iran have filled out an online questionnaire regarding the details of different imaging methods. The results after correcting and scoring this the questionnaires were subjected to statistical analysis. Results Only 5.2% of the questions about the amount of ionizing radiation in all types of imaging were correctly answered. Twenty two% of interns, 16% of general practitioners, and 23% of residents had accurate information about the side effects of diagnostic imaging. Ninety four% of participants were aware that the sonography method does not use ionizing radiation. Also, while 90% of participants were correct about the computed tomography (CT) scan, the percentage of those who were correct about the barium, positron emission tomography (PET), mammography, and Magnetic resonance imaging (MRI) was lower. Conclusion This study reveals a significant knowledge gap among Iranian medical professionals regarding ionizing radiation risks and dosages, despite frequent use of CT and PET scans. Findings highlight the need to enhance radiology education for safer diagnostic practices and better patient outcomes. This research provides a foundation for future studies on radiologic knowledge.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".