ASSESSMENT OF OCCUPATIONAL RADIATION DOSES OF MEDICAL RADIATION WORKERS IN TWO COMMUNITY HOSPITALS
Bibliographic record
Abstract
The International Commission on Radiological Protection recommends the adoption of the linear, no-threshold model as a predictive risk model for radiation protection purposes since the relationship between low-dose radiation exposure and cancer risk is unclear. Medical radiation workers are subject to occupational exposures and differences in workload, area of work and types of exposure can lead to variations in exposures between different occupational groups. We investigated the occupational exposures of 572 workers from four departments in two community hospitals and stratified into 22 occupational groups in order to identify groups with the highest radiation exposure. The occupational doses from 2015 to 2019 were analyzed to identify the dose distribution of each occupational group, total number of monitored workers, annual and collective deep (Hp(10)), eye (Hp(3)) and shallow (Hp(0.07)) doses. We further determined the individual and occupational group lifetime doses as well as the probability that monitored workers' lifetime doses will exceed a specified lifetime dose level. The occupational groups with the highest radiation exposures were the nuclear medicine technologists, diagnostic imaging radiologists and diagnostic cardiologists. Although our data suggest that occupational doses reported are low, it is essential that exposure of occupationally exposed personnel are always kept as low as reasonably achievable with an effective radiation protection program.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".