Assessment of the Impact of Low-dose Ionizing Radiation Exposure on Health Care Workers: A Study of Methods Used from a Scoping Review
Bibliographic record
Abstract
ABSTRACT: Healthcare workers may be exposed to ionizing radiation. Ionizing radiations are an important occupational risk factor for the potential damage they can cause to workers' health. Actually, the attention is focused on diseases caused by damage to radiosensitive organs. The aim of our study is to evaluate the methods used for the assessment of the impact of exposure to low-dose ionizing radiation in a population of healthcare workers (HCWs). The electronic database PubMed was searched by title, abstract, and medical subheadings (MeSH) terms. The extracted data were arranged into tables by dividing bibliographic reference, exposure, and statistical analysis information. The quality assessment was performed with the use of the Newcastle-Ottawa Quality Assessment Scale. The search strategy involved retrieving 15 studies (eight cohorts and seven cross-sectional studies). The univariate tests have been performed in 14 studies (93.3%), and Chi-square and T-test were the most commonly used. Multivariate tests have been performed in 11 studies (73.3%), and the most commonly performed were Logistic and Poisson Regressions. The most rated organ was the thyroid gland (six studies). The annual cumulative effective dose was the most used method to assess the dose rate (seven studies). Due to the characteristics of pathologies involved, a retrospective cohort study with an adequate control group and use of the annual cumulative effective dose to account for exposure could be useful features to obtain the best possible evidence. All the elements were found rarely in studies considered. The need is highlighted for more in-depth studies to investigate this topic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.065 | 0.201 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.042 | 0.040 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".