Analysis of the Environmental Impact of Radiation Doses on Dental and Oral Diseases (K03.6, K05.1, and K05.3) in Workers in the Indonesian National Nuclear Energy Agency
Bibliographic record
Abstract
Gamma radiation has a significant influence on oral health disorders.The effects of ionizing radiation can cause tooth decay, due to a reduction in saliva production and changes in the oral environment that can increase bacterial growth leading to dental caries, and enamel erosion.The study aims to determine the relationship between exposure to gamma radiation dose and length of work on the incidence of dental calculus (K03.6)periodontal (K05.3) and gingivitis (K05.1).We also measured smoking behavior and history of diabetes mellitus as confounding variables.The type of research used is objective correlation, to determine the relationship of gamma radiation exposure to oral and dental diseases.The research design is a retrospective-reference period cohort, data collection is carried out on cases from 2017 to 2019.Based on the results of the analysis of radiation dose in 2019, there was a significant relationship between radiation dose and calculus formation with a value of (R=0.503p=0.025).In 2018, dental calculus and gingivitis were significantly related, strong correlation, value (R=0.555p=0.001).In 2019, the incidence of gingivitis and radiation dose, there is a strong correlation and is significantly related, to the value (R=0.507p=0.021).Patients with a history of diabetes are very susceptible to periodontal and are not related to the length of work.It is recommended that radiation workers who have a history of diabetes and/or smoking take care of dental and oral conditions at least twice a year so that periodontal disease can be detected as early as possible.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".