title: Inferences, Risk Modeling, and Prediction of Health Effects of Ionizing Radiation.
abstract: The combined expertise of radiation epidemiologists and laboratory experimentalists is required to accurately define health risks from exposure to a low/very low radiation dose. Although stochastic risk can be estimated when a known threshold dose is exceeded, risk must be inferred from data transference at sub-threshold doses. The clinician's dilemma is evident when complying with accepted medical practice that is complicated by potential long-term, adverse outcomes. By contrast, radiation protection regulators must make prudent judgments without complete knowledge of the scope and consequences of their actions. Only by combining the strengths of epidemiological and experimental laboratory approaches can accurate predictive modeling be achieved after exposure to a low/very low dose.
authors.affiliation: *Radiation Emergency Assistance Center/Training Site, (REAC/TS), Oak Ridge, TN, Departments of Internal Medicine and Therapeutic Radiology, Yale University School of Medicine, New Haven, CT.
authors.forename: Nicholas
authors.initials: N
authors.lastname: Dainiak
mesh_terms: D003198:Computer Simulation; D004307:Dose-Response Relationship, Radiation; D015994:Incidence; D038622:Internationality; D008452:Maximum Allowable Concentration; D008954:Models, Biological; D000069079:Radiation Exposure / Q000032:analysis*; D011832:Radiation Injuries / Q000453:epidemiology*; D011839:Radiation, Ionizing; D011874:Radiometry / Q000379:methods*; D018570:Risk Assessment / Q000379:methods