Radiation science is dominated by a paradigm based on an assumption without empirical foundation. Known as the linear no-threshold (LNT) hypothesis, it holds that all ionizing radiation is harmful no matter how low the dose or dose rate. Epidemiological studies that claim to confirm LNT either neglect experimental and/or observational discoveries at the cellular, tissue, and organismal levels, or mention them only to distort or dismiss them. The appearance of validity in these studies rests on circular reasoning, cherry picking, faulty experimental design, and/or misleading inferences from weak statistical evidence. In contrast, studies based on biological discoveries demonstrate the reality of hormesis: the stimulation of biological responses that defend the organism against damage from environmental agents. Normal metabolic processes are far more damaging than all but the most extreme exposures to radiation. However, evolution has provided all extant plants and animals with defenses that repair such damage or remove the damaged cells, conferring on the organism even greater ability to defend against subsequent damage. Editors of medical journals now admit that perhaps half of the scientific literature may be untrue. Radiation science falls into that category. Belief in LNT informs the practice of radiology, radiation regulatory policies, and popular culture through the media. The result is mass radiophobia and harmful outcomes, including forced relocations of populations near nuclear power plant accidents, reluctance to avail oneself of needed medical imaging studies, and aversion to nuclear energy—all unwarranted and all harmful to millions of people.
The linear no-threshold assumption misunderstands the complex multiphasic biological response to ionizing radiation, focusing solely on the initial physical radiogenic damage. This misunderstanding is enabled (masked and amplified) by a number of mathematical approaches that bias results in favor of linear no-threshold and away from alternatives, like hormesis, that take biological response into account. Here we explore a number of these mathematical approaches in some detail, including the use of frequentist rather than Bayesian statistical rules and methods. We argue that a Bayesian approach cuts through an epidemiological stalemate, in part because it enables a better understanding of the concept of plausibility, which in turn properly rests on empirical evidence of actual physical and biological mechanisms. Misuse of the concept of plausibility has sometimes been used to justify the mathematically simple and convenient linearity-without-a-threshold assumption, in particular with the everywhere-positive slope that is central to linear no-threshold and its variants. Linear no-threshold’s dominance in the area of dose regulation further rests on a misapplication of the precautionary principle, which only holds when a putative caution has positive effects that outweigh the negative unintended consequences. In this case the negative consequences far outweigh the presumed hazards.
A 21st century workforce must be trained to solve not only major national and global challenges but to fit into the current complex work environment. The challenges cannot be solved by a single discipline and require interdisciplinary solutions only possible through the collaboration of physical, biological, and social scientists along with engineers. The 21st century student needs to be educated so they can combine disciplinary depth with the ability to reach out to other disciplines. Such training requires a cost-effective higher education structure that promotes and sustains interdisciplinary research and education (IDRE). The current structure has failed to achieve this. In response to this failure, some private universities like Dartmouth and Olin and public institutions like the University of California Merced (UC Merced) are experimenting with giving up traditional department silos and majors in favor of an interdisciplinary organization. These programs can serve as models for what is to be done in U.S. higher education and may also serve as models for emerging universities in the developing world.
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