Perception of Radiation Exposure and Risk among Patients, Medical Students, and Referring Physicians at a Tertiary Care Community Hospital
Bibliographic record
Abstract
BACKGROUND: It is important for physicians to be aware of the radiation doses as well as the risks associated with diagnostic imaging procedures that they are ordering. METHODS: A survey was administered to patients, medical students, and referring physicians from a number of specialties to determine background knowledge regarding radiation exposure and risk associated with commonly ordered medical imaging tests. RESULTS: A total of 127 patients, 32 referring physicians, and 30 medical students completed the survey. The majority of patients (92%) were not informed of the radiation risks associated with tests that they were scheduled to receive and had false perceptions about the use of radiation and its associated risks. Physicians and medical students had misconceptions about the use of ionizing radiation in a number of radiologic examinations; for example, 25% and 43% of physicians and medical students, respectively, were unaware that interventional procedures used ionizing radiation, and 28% of physicians were unaware that mammography used ionizing radiation. Computed tomographies and barium studies were thought to be associated with the least ionizing radiation among physicians. CONCLUSION: There is a need for educating the public, medical students, and referring physicians about radiation exposure and associated risk so that (1) patients receiving multiple medical imaging tests are aware of the radiation that they are receiving and (2) physicians and future physicians will make informed decisions when ordering such tests to limit the amount of radiation that patients receive and to promote informed consent among patients.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".