Sex Difference of Radiation Response in Occupational and Accidental Exposure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ionizing radiation is a well-established cause of deleterious effects on human health. Understanding the risks of radiation exposure is important for the development of protective measures and guidelines. Demographic factors such as age, sex, genetic susceptibility, comorbidities, and various other lifestyle factors influence the radiosensitivity of different subpopulations. Amongst these factors, the influence of sex differences on radiation sensitivity has been given very less attention. In fact, the International Commission on Radiological Protection (ICRP) has based its recommendations on a population average, rather than the data on the radiosensitivity of distinct subpopulations. In this study, we reviewed major human studies on the health risks of radiation exposure and showed that sex-related factors may potentially influence the long-term response to radiation exposure. Available data suggest that long-term radiosensitivity in women is higher than that in men who receive a comparable dose of radiation. The report on the biological effects of ionizing radiation (BEIR VII) published in 2006 by the National Academy of Sciences, United States emphasized that women may be at significantly greater risk of suffering and dying from radiation-induced cancer than men exposed to the same dose of radiation. We show that radiation effects are sex-specific, and long-term radiosensitivity in females is higher than that in males. We also discuss the radiation effects as a function of age. In the future, more systematic studies are needed to elucidate the sex differences in radiation responses across the life continuum - from preconception through childhood, adulthood, and old age - to ensure that boys and girls and men and women are equally protected across ages.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 it