Assessment of radiation exposure in a nuclear medicine department of an oncology hospital
Bibliographic record
Abstract
Background: Medical personnel in nuclear medicine departments are exposed to ionizing radiation which is associated with cancer induction and mortality risks. Examples of such personnel are those working in the hot-areas where radioisotopes for nuclear medicine examinations are prepared. This work aims to estimate the occupational radiation exposure for workers at the Nuclear Medicine department in Da Nang Oncology Hospital, Vietnam. Materials and methods: The study was conducted from 2020 to 2021. The personal dose equivalents (Hp) were quantified for soft tissues for deep dose (Hp(10)), eye lens dose (Hp(3)), skin dose (and Hp(0.07)) were quantified utilizing optically stimulated luminescence dosimeters (OSLD) crystals. In this study, the dose equivalent at the hot area is measured by 9 OSLD, while that for staff was measured using 16 OSLD. Results: In 2020, the annual average Hp(10) was (0.38 mSv ± 1.39); in 2021 Hp(10) was (0.25 mSv ± 1.27). The average Hp(10) was well below the annual occupational dose limits (20 mSv.year−1). Regarding the annual dose, all recorded values were quite low compared to the annual radiation dose limits for radiation staff (20 mSv annual ∼ 1.67 mSv.month−1). Nevertheless, some points of measurement revealed a high-value dose of 5.69 mSv ± 2.83. This suggests that rigorous evaluation of the workplace is essential to ensuring workers' exposure is kept within safe levels. Conclusions: It is concluded that the Hp(10) and Hp(0.07) for the working environment and radiation workers were below the dose limits from ICRP. In addition, it is recommended that continuous monitoring of the occupational dose in nuclear medicine department is essential to reduce the dosimetric hazards of personnel working in the department.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".