X-ray therapy safety and awareness education for medical trainees and attending physicians
Bibliographic record
Abstract
INTRODUCTION: Medical imaging involving ionizing radiation is common in the clinical setting. Little is known about the level of radiation safety training for medical trainees and attending physicians. We sought to identify the level of radiation safety knowledge and training at the undergraduate, postgraduate, and attending physician level. METHODS: A 29-question survey was sent by email to two sites in Canada. We pooled the results of medical students, residents, and attending physicians. The primary outcome was to describe the amount of radiation safety training among these groups. The secondary outcomes were to describe the frequency of radiation exposure, level of radiation knowledge, and preferred training method for radiation safety. RESULTS: Of 115 surveys that were properly completed, 31 (26.9%) medical students, 17 (14.7%) residents, and 67 (58.3%) attending physicians responded. A greater number of medical students (41.9%) reported they had zero hours of training time for radiation safety compared to attending physicians (14.9%) (p<0.05). A higher number of attending physicians (47.8%) and residents (64.7%) participated in patient care involving fluoroscopy daily or at least several times per week compared to medical students (3.2%) (p<0.001). Attending physicians had the greatest number of correct responses to radiation safety questions. Online courses and workshops were the preferred training methods. CONCLUSIONS: Radiation safety training is an important component of medical education for medical trainees and attending physicians. Current radiation safety training requirements and procedures at various levels of medical training in Canada should be addressed. Implementing radiation safety education may improve adherence to the radiation safety principles.
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".